From 12ff7d64f8e57e1e20fa427a34ec20e86876312d Mon Sep 17 00:00:00 2001
From: Alexander Nasuta <alexander.nasuta@ima.rwth-aachen.de>
Date: Thu, 20 Mar 2025 15:37:21 +0100
Subject: [PATCH] docs

---
 README.md                                     |  383 ++++-
 docs/source/_static/ft06-std.txt              |    7 +
 docs/source/_static/ft06-ta.txt               |   13 +
 docs/source/conf.py                           |    6 +-
 docs/source/examples/JSSEnv_example.ipynb     |  180 +++
 docs/source/examples/example-gallery.md       |   13 +
 .../examples/graph-jsp-env_example.ipynb      |  119 ++
 .../graph-matrix-jsp-env_example.ipynb        |  112 ++
 docs/source/examples/or-tools_example.ipynb   |  114 ++
 docs/source/index.md                          |   16 +
 docs/source/index.rst                         |   17 -
 docs/source/instances-table.md                |  369 +++++
 docs/source/related/kemmerling.md             |    6 +
 docs/source/related/related-projects.md       |   12 +
 docs/source/related/ruby.md                   |    3 +
 docs/source/related/weise.md                  |    4 +
 .../source-code-docs/instance-download.md     |   32 +
 .../source-code-docs/instance-generator.md    |   10 +
 .../source-code-docs/or-tools-solver.md       |   11 +
 docs/source/source-code-docs/parser.md        |   28 +
 docs/source/source-code-docs/source-code.md   |   14 +
 examples/JSSEnv_example.py                    |   47 +
 examples/graph-matrix-jsp-env_example.py      |   29 +
 examples/kemmerling_example.py                |    0
 examples/or-tools_example.py                  |   12 +
 pyproject.toml                                |    3 +
 requirements_dev.txt                          |   20 +-
 resources/bibliography.bib                    | 1236 +++++++++++++++++
 src/jsp_instance_utils/jsp_or_tools_solver.py |   20 +
 29 files changed, 2809 insertions(+), 27 deletions(-)
 create mode 100644 docs/source/_static/ft06-std.txt
 create mode 100644 docs/source/_static/ft06-ta.txt
 create mode 100644 docs/source/examples/JSSEnv_example.ipynb
 create mode 100644 docs/source/examples/example-gallery.md
 create mode 100644 docs/source/examples/graph-jsp-env_example.ipynb
 create mode 100644 docs/source/examples/graph-matrix-jsp-env_example.ipynb
 create mode 100644 docs/source/examples/or-tools_example.ipynb
 create mode 100644 docs/source/index.md
 delete mode 100644 docs/source/index.rst
 create mode 100644 docs/source/instances-table.md
 create mode 100644 docs/source/related/kemmerling.md
 create mode 100644 docs/source/related/related-projects.md
 create mode 100644 docs/source/related/ruby.md
 create mode 100644 docs/source/related/weise.md
 create mode 100644 docs/source/source-code-docs/instance-download.md
 create mode 100644 docs/source/source-code-docs/instance-generator.md
 create mode 100644 docs/source/source-code-docs/or-tools-solver.md
 create mode 100644 docs/source/source-code-docs/parser.md
 create mode 100644 docs/source/source-code-docs/source-code.md
 delete mode 100644 examples/kemmerling_example.py
 create mode 100644 resources/bibliography.bib

diff --git a/README.md b/README.md
index ba241d2..7b04be5 100644
--- a/README.md
+++ b/README.md
@@ -5,11 +5,7 @@
 <!-- PROJECT LOGO -->
 <br />
 <div align="center">
-   <!--
-  <a href="https://cybernetics-lab.de/">
-    <img src="https://github.com/Alexander-Nasuta/graph-jsp-env/raw/master/resources/readme_images/logo.png">
-  </a>
-   -->
+
 
   <h1 align="center">
      Job Shop Scheduling Problem Benchmark Instances
@@ -31,7 +27,7 @@ For more info about the formatting see: http://jobshop.jjvh.nl/explanation.php
 The benchmark instances can be imported as a numpy array.
 The instances have the shape `(2, n_jobs, n_machines)`, where the last dimension contains the processing time and the release time of the job on the machine.
 So the instance-numpy-array consists of two numpy arrays, the first array contains the order of the machines and the second array contains the processing times of the tasks on the machines.
-here is a minimal example of how to define a instance:
+Here is a minimal example of how to define a instance:
 
 ```python
 import numpy as np
@@ -80,6 +76,380 @@ You can also find them on this website: http://jobshop.jjvh.nl/
 # More Examples
 For more examples you can have a look at the test files in the `tests` directory.
 
+## Instance Information and Statistics
+
+This section is based on the GitHub Repo [jsspInstancesAndResults](https://github.com/thomasWeise/jsspInstancesAndResults) by Thomas Weise.
+The linked Repo is essentially a equivalent to this one, but for the programming language R.
+
+The rows have the following meaning:
+
+- `id` the unique identifier of the instance, as used in the literature (unsolved instances are marked in **bold**)
+- `ref` the reference to the publication where the instance was first mentioned/created
+- `jobs` the number of jobs in the instance
+- `machines` the number of machines in the instance
+- `lb` the lower bound for the makespan of any solution for the instance
+- `lb ref` the reference to the earliest publication (in this survey) that mentioned this lower bound
+- `bks` the makespan of the best-known solution (in terms of the makespan), based on this survey
+- `bks ref` the reference(s) to the earliest publication(s) in this survey that mentioned the bks
+- `t(bks) in s` the fastest time reported (in seconds), by any of the references in the study, for reaching `bks`
+- `t(bks) ref` the reference(s) of the publications reporting `t(bks)`
+
+Please, please take the column `t(bks)` with many grains of salt.
+First, we just report the time, regardless of which computer was used to obtain the result or even whether parallelism was applied or not.
+Second sometimes a minimum time to reach the best result of the run is given in a paper, sometimes we just have the maximum runtime used, sometimes we have a buget &ndash; and some publications do not report a runtime at all.
+Hence, our data here is very incomplete and unreliable and for some instances, we may not have any proper runtime value at all
+Therefore, this column is not to be understood as a normative a reliable information, more as a very rough guide regarding where we are standing right now.
+And, needless to say, it is only populated with the information extracted from the papers used in this study, so it may not even be representative.
+
+|id|ref|jobs|machines|lb|lb ref|bks|bks ref|t(bks) in s|t(bks) ref|
+|---:|:---:|---:|---:|---:|:---:|---:|:---:|---:|:---:|
+|abz5|<a href="#ABZ">ABZ</a>|10|10|1234|<a href="#AC">AC</a>|1234|<a href="#AC">AC</a>|0.04|<a href="#AZ">AZ</a>|
+|abz6|<a href="#ABZ">ABZ</a>|10|10|943|<a href="#AC">AC</a>|943|<a href="#AC">AC</a>|0.03|<a href="#AZ">AZ</a>|
+|abz7|<a href="#ABZ">ABZ</a>|20|15|656|<a href="#M">M</a>|656|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|**abz8**|<a href="#ABZ">ABZ</a>|20|15|**648**|<a href="#VLS">VLS</a>|**665**|<a href="#H">H</a>|**1000**|<a href="#H">H</a>|
+|abz9|<a href="#ABZ">ABZ</a>|20|15|678|<a href="#KNF">KNF</a>|678|<a href="#ZSR">ZSR</a>|3.25|<a href="#AZ">AZ</a>|
+|**dmu01**|<a href="#DMU1">DMU1</a>|20|15|**2501**|<a href="#BB">BB</a>|**2563**|<a href="#H">H</a>|**332.87**|<a href="#PLC">PLC</a>|
+|**dmu02**|<a href="#DMU1">DMU1</a>|20|15|**2651**|<a href="#BB">BB</a>|**2706**|<a href="#H">H</a>|**179.24**|<a href="#PLC">PLC</a>|
+|dmu03|<a href="#DMU1">DMU1</a>|20|15|2731|<a href="#BB">BB</a>|2731|<a href="#H">H</a>|388.59|<a href="#PLC">PLC</a>|
+|**dmu04**|<a href="#DMU1">DMU1</a>|20|15|**2601**|<a href="#BB">BB</a>|**2669**|<a href="#H">H</a>|**96.54**|<a href="#PLC">PLC</a>|
+|dmu05|<a href="#DMU1">DMU1</a>|20|15|2749|<a href="#BB">BB</a>|2749|<a href="#H">H</a>|303|<a href="#PLC">PLC</a>|
+|**dmu06**|<a href="#DMU1">DMU1</a>|20|20|**3042**|<a href="#vH2">vH2</a>|**3244**|<a href="#PSV">PSV</a>|**10000**|<a href="#PSV">PSV</a>|
+|**dmu07**|<a href="#DMU1">DMU1</a>|20|20|**2828**|<a href="#vH2">vH2</a>|**3046**|<a href="#PSV">PSV</a>|**360.58**|<a href="#PLC">PLC</a>|
+|**dmu08**|<a href="#DMU1">DMU1</a>|20|20|**3051**|<a href="#GL">GL</a>|**3188**|<a href="#PSV">PSV</a>|**295.81**|<a href="#PLC">PLC</a>|
+|**dmu09**|<a href="#DMU1">DMU1</a>|20|20|**2956**|<a href="#GL">GL</a>|**3092**|<a href="#H">H</a>|**500**|<a href="#H">H</a>|
+|**dmu10**|<a href="#DMU1">DMU1</a>|20|20|**2858**|<a href="#GL">GL</a>|**2984**|<a href="#PSV">PSV</a>|**10000**|<a href="#PSV">PSV</a>|
+|**dmu11**|<a href="#DMU1">DMU1</a>|30|15|**3395**|<a href="#DMU">DMU</a>|**3430**|<a href="#PLC">PLC</a>|**1496.85**|<a href="#PLC">PLC</a>|
+|**dmu12**|<a href="#DMU1">DMU1</a>|30|15|**3481**|<a href="#DMU">DMU</a>|**3492**|<a href="#SS">SS</a>|||
+|dmu13|<a href="#DMU1">DMU1</a>|30|15|3681|<a href="#DMU">DMU</a>|3681|<a href="#GR">GR</a>|622.13|<a href="#PLC">PLC</a>|
+|dmu14|<a href="#DMU1">DMU1</a>|30|15|3394|<a href="#DMU">DMU</a>|3394|<a href="#H">H</a>|3.02|<a href="#PLC">PLC</a>|
+|dmu15|<a href="#DMU1">DMU1</a>|30|15|3343|<a href="#GL">GL</a>|3343|<a href="#H">H</a>|1.77|<a href="#PLC">PLC</a>|
+|**dmu16**|<a href="#DMU1">DMU1</a>|30|20|**3734**|<a href="#GL">GL</a>|**3751**|<a href="#GR">GR</a>|||
+|**dmu17**|<a href="#DMU1">DMU1</a>|30|20|**3709**|<a href="#GL">GL</a>|**3814**|<a href="#SS">SS</a>|||
+|dmu18|<a href="#DMU1">DMU1</a>|30|20|3844|<a href="#DMU">DMU</a>|3844|<a href="#GR">GR</a>|3787.4|<a href="#PLC">PLC</a>|
+|**dmu19**|<a href="#DMU1">DMU1</a>|30|20|**3672**|<a href="#vH2">vH2</a>|**3765**|<a href="#SS">SS</a>|||
+|**dmu20**|<a href="#DMU1">DMU1</a>|30|20|**3604**|<a href="#DMU">DMU</a>|**3710**|<a href="#PLC">PLC</a>|**701.29**|<a href="#PLC">PLC</a>|
+|dmu21|<a href="#DMU1">DMU1</a>|40|15|4380|<a href="#DMU">DMU</a>|4380|<a href="#H">H</a>|0.69|<a href="#PLC">PLC</a>|
+|dmu22|<a href="#DMU1">DMU1</a>|40|15|4725|<a href="#DMU">DMU</a>|4725|<a href="#H">H</a>|1.48|<a href="#PLC">PLC</a>|
+|dmu23|<a href="#DMU1">DMU1</a>|40|15|4668|<a href="#DMU">DMU</a>|4668|<a href="#H">H</a>|1.3|<a href="#PLC">PLC</a>|
+|dmu24|<a href="#DMU1">DMU1</a>|40|15|4648|<a href="#DMU">DMU</a>|4648|<a href="#H">H</a>|0.75|<a href="#PLC">PLC</a>|
+|dmu25|<a href="#DMU1">DMU1</a>|40|15|4164|<a href="#DMU">DMU</a>|4164|<a href="#H">H</a>|0.6|<a href="#PLC">PLC</a>|
+|dmu26|<a href="#DMU1">DMU1</a>|40|20|4647|<a href="#DMU">DMU</a>|4647|<a href="#GR">GR</a>|1631.43|<a href="#PLC">PLC</a>|
+|dmu27|<a href="#DMU1">DMU1</a>|40|20|4848|<a href="#DMU">DMU</a>|4848|<a href="#H">H</a>|12.16|<a href="#PLC">PLC</a>|
+|dmu28|<a href="#DMU1">DMU1</a>|40|20|4692|<a href="#DMU">DMU</a>|4692|<a href="#H">H</a>|17.68|<a href="#PLC">PLC</a>|
+|dmu29|<a href="#DMU1">DMU1</a>|40|20|4691|<a href="#DMU">DMU</a>|4691|<a href="#H">H</a>|63.49|<a href="#PLC">PLC</a>|
+|dmu30|<a href="#DMU1">DMU1</a>|40|20|4732|<a href="#DMU">DMU</a>|4732|<a href="#H">H</a>|123|<a href="#PLC">PLC</a>|
+|dmu31|<a href="#DMU1">DMU1</a>|50|15|5640|<a href="#DMU">DMU</a>|5640|<a href="#H">H</a>|0.84|<a href="#PLC">PLC</a>|
+|dmu32|<a href="#DMU1">DMU1</a>|50|15|5927|<a href="#DMU">DMU</a>|5927|<a href="#H">H</a>|0.62|<a href="#PLC">PLC</a>|
+|dmu33|<a href="#DMU1">DMU1</a>|50|15|5728|<a href="#DMU">DMU</a>|5728|<a href="#H">H</a>|0.43|<a href="#PLC">PLC</a>|
+|dmu34|<a href="#DMU1">DMU1</a>|50|15|5385|<a href="#DMU">DMU</a>|5385|<a href="#H">H</a>|2.22|<a href="#PLC">PLC</a>|
+|dmu35|<a href="#DMU1">DMU1</a>|50|15|5635|<a href="#DMU">DMU</a>|5635|<a href="#H">H</a>|0.71|<a href="#PLC">PLC</a>|
+|dmu36|<a href="#DMU1">DMU1</a>|50|20|5621|<a href="#DMU">DMU</a>|5621|<a href="#H">H</a>|7.83|<a href="#PLC">PLC</a>|
+|dmu37|<a href="#DMU1">DMU1</a>|50|20|5851|<a href="#DMU">DMU</a>|5851|<a href="#H">H</a>|11.38|<a href="#PLC">PLC</a>|
+|dmu38|<a href="#DMU1">DMU1</a>|50|20|5713|<a href="#DMU">DMU</a>|5713|<a href="#H">H</a>|10.66|<a href="#PLC">PLC</a>|
+|dmu39|<a href="#DMU1">DMU1</a>|50|20|5747|<a href="#DMU">DMU</a>|5747|<a href="#H">H</a>|2.02|<a href="#PLC">PLC</a>|
+|dmu40|<a href="#DMU1">DMU1</a>|50|20|5577|<a href="#DMU">DMU</a>|5577|<a href="#H">H</a>|4.91|<a href="#PLC">PLC</a>|
+|**dmu41**|<a href="#DMU1">DMU1</a>|20|15|**3007**|<a href="#GL">GL</a>|**3248**|<a href="#PLC">PLC</a>|**417.84**|<a href="#PLC">PLC</a>|
+|**dmu42**|<a href="#DMU1">DMU1</a>|20|15|**3224**|<a href="#vH2">vH2</a>|**3390**|<a href="#PLC">PLC</a>|**448.95**|<a href="#PLC">PLC</a>|
+|**dmu43**|<a href="#DMU1">DMU1</a>|20|15|**3292**|<a href="#GL">GL</a>|**3441**|<a href="#GR">GR</a>|**399.33**|<a href="#PLC">PLC</a>|
+|**dmu44**|<a href="#DMU1">DMU1</a>|20|15|**3299**|<a href="#vH2">vH2</a>|**3475**|<a href="#SS">SS</a>|||
+|**dmu45**|<a href="#DMU1">DMU1</a>|20|15|**3039**|<a href="#vH2">vH2</a>|**3272**|<a href="#GR">GR</a>|||
+|**dmu46**|<a href="#DMU1">DMU1</a>|20|20|**3575**|<a href="#GL">GL</a>|**4035**|<a href="#GR">GR</a>|**984.86**|<a href="#PLC">PLC</a>|
+|**dmu47**|<a href="#DMU1">DMU1</a>|20|20|**3522**|<a href="#GL">GL</a>|**3939**|<a href="#GR">GR</a>|||
+|**dmu48**|<a href="#DMU1">DMU1</a>|20|20|**3447**|<a href="#GL">GL</a>|**3763**|<a href="#SS">SS</a>|||
+|**dmu49**|<a href="#DMU1">DMU1</a>|20|20|**3403**|<a href="#GL">GL</a>|**3710**|<a href="#PLC">PLC</a>|**633.84**|<a href="#PLC">PLC</a>|
+|**dmu50**|<a href="#DMU1">DMU1</a>|20|20|**3496**|<a href="#GL">GL</a>|**3729**|<a href="#PLC">PLC</a>|**609.62**|<a href="#PLC">PLC</a>|
+|**dmu51**|<a href="#DMU1">DMU1</a>|30|15|**3954**|<a href="#vH2">vH2</a>|**4156**|<a href="#SS">SS</a>|||
+|**dmu52**|<a href="#DMU1">DMU1</a>|30|15|**4094**|<a href="#vH2">vH2</a>|**4311**|<a href="#PLC">PLC</a>|**2232.6**|<a href="#PLC">PLC</a>|
+|**dmu53**|<a href="#DMU1">DMU1</a>|30|15|**4141**|<a href="#GL">GL</a>|**4390**|<a href="#SS">SS</a>|||
+|**dmu54**|<a href="#DMU1">DMU1</a>|30|15|**4202**|<a href="#GL">GL</a>|**4362**|<a href="#SS">SS</a>|||
+|**dmu55**|<a href="#DMU1">DMU1</a>|30|15|**4146**|<a href="#vH2">vH2</a>|**4270**|<a href="#SS">SS</a>|||
+|**dmu56**|<a href="#DMU1">DMU1</a>|30|20|**4554**|<a href="#GL">GL</a>|**4941**|<a href="#PLC">PLC</a>|**3825.44**|<a href="#PLC">PLC</a>|
+|**dmu57**|<a href="#DMU1">DMU1</a>|30|20|**4302**|<a href="#GL">GL</a>|**4663**|<a href="#PLC">PLC</a>|**3649.41**|<a href="#PLC">PLC</a>|
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+|**dmu59**|<a href="#DMU1">DMU1</a>|30|20|**4219**|<a href="#vH2">vH2</a>|**4619**|<a href="#SS">SS</a>|||
+|**dmu60**|<a href="#DMU1">DMU1</a>|30|20|**4319**|<a href="#GL">GL</a>|**4739**|<a href="#SS">SS</a>|||
+|**dmu61**|<a href="#DMU1">DMU1</a>|40|15|**4917**|<a href="#GL">GL</a>|**5172**|<a href="#SS">SS</a>|||
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+|**dmu64**|<a href="#DMU1">DMU1</a>|40|15|**5130**|<a href="#DMU">DMU</a>|**5240**|<a href="#SS">SS</a>|||
+|**dmu65**|<a href="#DMU1">DMU1</a>|40|15|**5107**|<a href="#vH2">vH2</a>|**5190**|<a href="#SS">SS</a>|||
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+|**dmu70**|<a href="#DMU1">DMU1</a>|40|20|**5501**|<a href="#GL">GL</a>|**5889**|<a href="#SS">SS</a>|||
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+|**dmu72**|<a href="#DMU1">DMU1</a>|50|15|**6395**|<a href="#GL">GL</a>|**6463**|<a href="#SS">SS</a>|||
+|**dmu73**|<a href="#DMU1">DMU1</a>|50|15|**6001**|<a href="#GL">GL</a>|**6153**|<a href="#SS">SS</a>|||
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+|la06|<a href="#L">L</a>|15|5|926|<a href="#ABZ">ABZ</a>|926|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la07|<a href="#L">L</a>|15|5|890|<a href="#ABZ">ABZ</a>|890|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la08|<a href="#L">L</a>|15|5|863|<a href="#ABZ">ABZ</a>|863|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la09|<a href="#L">L</a>|15|5|951|<a href="#ABZ">ABZ</a>|951|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la10|<a href="#L">L</a>|15|5|958|<a href="#ABZ">ABZ</a>|958|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la11|<a href="#L">L</a>|20|5|1222|<a href="#ABZ">ABZ</a>|1222|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la12|<a href="#L">L</a>|20|5|1039|<a href="#ABZ">ABZ</a>|1039|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la13|<a href="#L">L</a>|20|5|1150|<a href="#ABZ">ABZ</a>|1150|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la14|<a href="#L">L</a>|20|5|1292|<a href="#ABZ">ABZ</a>|1292|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la15|<a href="#L">L</a>|20|5|1207|<a href="#ABZ">ABZ</a>|1207|<a href="#AC">AC</a>|0.016|<a href="#AZ">AZ</a>|
+|la16|<a href="#L">L</a>|10|10|945|<a href="#CP1">CP1</a>|945|<a href="#AC">AC</a>|0.06|<a href="#CCC">CCC</a>|
+|la17|<a href="#L">L</a>|10|10|784|<a href="#CP1">CP1</a>|784|<a href="#AC">AC</a>|0.016|<a href="#AZ">AZ</a>|
+|la18|<a href="#L">L</a>|10|10|848|<a href="#AC">AC</a>|848|<a href="#AC">AC</a>|0.015|<a href="#AZ">AZ</a>|
+|la19|<a href="#L">L</a>|10|10|842|<a href="#AC">AC</a>|842|<a href="#AC">AC</a>|0.025|<a href="#AZ">AZ</a>|
+|la20|<a href="#L">L</a>|10|10|902|<a href="#AC">AC</a>|902|<a href="#AC">AC</a>|0.031|<a href="#AZ">AZ</a>|
+|la21|<a href="#L">L</a>|15|10|1046|<a href="#VAL">VAL</a>|1046|<a href="#YN1">YN1</a>|7.33|<a href="#PLC">PLC</a>|
+|la22|<a href="#L">L</a>|15|10|927|<a href="#AC">AC</a>|927|<a href="#AC">AC</a>|0.109|<a href="#AZ">AZ</a>|
+|la23|<a href="#L">L</a>|15|10|1032|<a href="#ABZ">ABZ</a>|1032|<a href="#AC">AC</a>|0.047|<a href="#AZ">AZ</a>|
+|la24|<a href="#L">L</a>|15|10|935|<a href="#AC">AC</a>|935|<a href="#AC">AC</a>|0.2|<a href="#AZ">AZ</a>|
+|la25|<a href="#L">L</a>|15|10|977|<a href="#AC">AC</a>|977|<a href="#AC">AC</a>|0.33|<a href="#AZ">AZ</a>|
+|la26|<a href="#L">L</a>|20|10|1218|<a href="#ABZ">ABZ</a>|1218|<a href="#AC">AC</a>|0.078|<a href="#AZ">AZ</a>|
+|la27|<a href="#L">L</a>|20|10|1235|<a href="#ABZ">ABZ</a>|1235|<a href="#YN1">YN1</a>|0.95|<a href="#AZ">AZ</a>|
+|la28|<a href="#L">L</a>|20|10|1216|<a href="#ABZ">ABZ</a>|1216|<a href="#AC">AC</a>|0.109|<a href="#AZ">AZ</a>|
+|la29|<a href="#L">L</a>|20|10|1152|<a href="#M">M</a>|1152|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|la30|<a href="#L">L</a>|20|10|1355|<a href="#ABZ">ABZ</a>|1355|<a href="#AC">AC</a>|0.093|<a href="#AZ">AZ</a>|
+|la31|<a href="#L">L</a>|30|10|1784|<a href="#ABZ">ABZ</a>|1784|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la32|<a href="#L">L</a>|30|10|1850|<a href="#ABZ">ABZ</a>|1850|<a href="#AC">AC</a>|0.047|<a href="#AZ">AZ</a>|
+|la33|<a href="#L">L</a>|30|10|1719|<a href="#ABZ">ABZ</a>|1719|<a href="#AC">AC</a>|0.031|<a href="#AZ">AZ</a>|
+|la34|<a href="#L">L</a>|30|10|1721|<a href="#ABZ">ABZ</a>|1721|<a href="#AC">AC</a>|0.156|<a href="#AZ">AZ</a>|
+|la35|<a href="#L">L</a>|30|10|1888|<a href="#ABZ">ABZ</a>|1888|<a href="#AC">AC</a>|0.046|<a href="#AZ">AZ</a>|
+|la36|<a href="#L">L</a>|15|15|1268|<a href="#CP1">CP1</a>|1268|<a href="#AC">AC</a>|0.57|<a href="#AZ">AZ</a>|
+|la37|<a href="#L">L</a>|15|15|1397|<a href="#AC">AC</a>|1397|<a href="#AC">AC</a>|0.51|<a href="#AZ">AZ</a>|
+|la38|<a href="#L">L</a>|15|15|1196|<a href="#VAL">VAL</a>|1196|<a href="#NS">NS</a>|1.25|<a href="#AZ">AZ</a>|
+|la39|<a href="#L">L</a>|15|15|1233|<a href="#AC">AC</a>|1233|<a href="#AC">AC</a>|0.5|<a href="#AZ">AZ</a>|
+|la40|<a href="#L">L</a>|15|15|1222|<a href="#AC">AC</a>|1222|<a href="#AC">AC</a>|384.8|<a href="#PLC">PLC</a>|
+|orb01|<a href="#AC">AC</a>|10|10|1059|<a href="#AC">AC</a>|1059|<a href="#AC">AC</a>|0.06|<a href="#AZ">AZ</a>|
+|orb02|<a href="#AC">AC</a>|10|10|888|<a href="#AC">AC</a>|888|<a href="#AC">AC</a>|0.06|<a href="#AZ">AZ</a>|
+|orb03|<a href="#AC">AC</a>|10|10|1005|<a href="#AC">AC</a>|1005|<a href="#AC">AC</a>|0.15|<a href="#AZ">AZ</a>|
+|orb04|<a href="#AC">AC</a>|10|10|1005|<a href="#AC">AC</a>|1005|<a href="#AC">AC</a>|0.1|<a href="#CCC">CCC</a>|
+|orb05|<a href="#AC">AC</a>|10|10|887|<a href="#AC">AC</a>|887|<a href="#AC">AC</a>|0.76|<a href="#AZ">AZ</a>|
+|orb06|<a href="#AC">AC</a>|10|10|1010|<a href="#JM">JM</a>|1010|<a href="#BV1">BV1</a>|0.72|<a href="#AZ">AZ</a>|
+|orb07|<a href="#AC">AC</a>|10|10|397|<a href="#JM">JM</a>|397|<a href="#H">H</a>|0.02|<a href="#AZ">AZ</a>|
+|orb08|<a href="#AC">AC</a>|10|10|899|<a href="#JM">JM</a>|899|<a href="#BV1">BV1</a>|0.09|<a href="#AZ">AZ</a>|
+|orb09|<a href="#AC">AC</a>|10|10|934|<a href="#JM">JM</a>|934|<a href="#BV1">BV1</a>|0.09|<a href="#AZ">AZ</a>|
+|orb10|<a href="#AC">AC</a>|10|10|944|<a href="#JM">JM</a>|944|<a href="#BV1">BV1</a>|0.03|<a href="#AZ">AZ</a>|
+|swv01|<a href="#SWV">SWV</a>|20|10|1407|<a href="#M">M</a>|1407|<a href="#H">H</a>|575.76|<a href="#PLC">PLC</a>|
+|swv02|<a href="#SWV">SWV</a>|20|10|1475|<a href="#M">M</a>|1475|<a href="#H">H</a>|136.94|<a href="#AZ">AZ</a>|
+|swv03|<a href="#SWV">SWV</a>|20|10|1398|<a href="#BB">BB</a>|1398|<a href="#H">H</a>|613|<a href="#PLC">PLC</a>|
+|swv04|<a href="#SWV">SWV</a>|20|10|1464|<a href="#VLS">VLS</a>|1464|<a href="#VLS2">VLS2</a>|30000|<a href="#VLS2">VLS2</a>|
+|swv05|<a href="#SWV">SWV</a>|20|10|1424|<a href="#M">M</a>|1424|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|**swv06**|<a href="#SWV">SWV</a>|20|15|**1630**|<a href="#VLS">VLS</a>|**1671**|<a href="#PLC">PLC</a>, <a href="#VLS2">VLS2</a>|**385.73**|<a href="#PLC">PLC</a>|
+|**swv07**|<a href="#SWV">SWV</a>|20|15|**1513**|<a href="#VLS">VLS</a>|**1594**|<a href="#GR">GR</a>|||
+|**swv08**|<a href="#SWV">SWV</a>|20|15|**1671**|<a href="#VLS">VLS</a>|**1752**|<a href="#PLC">PLC</a>, <a href="#VLS2">VLS2</a>|**503**|<a href="#PLC">PLC</a>|
+|**swv09**|<a href="#SWV">SWV</a>|20|15|**1633**|<a href="#VLS">VLS</a>|**1655**|<a href="#PLC">PLC</a>, <a href="#VLS2">VLS2</a>|**521.91**|<a href="#PLC">PLC</a>|
+|**swv10**|<a href="#SWV">SWV</a>|20|15|**1663**|<a href="#VLS">VLS</a>|**1743**|<a href="#GR">GR</a>|**441.4**|<a href="#PLC">PLC</a>|
+|swv11|<a href="#SWV">SWV</a>|50|10|2983|<a href="#V1">V1</a>|2983|<a href="#NS2">NS2</a>|940.68|<a href="#PLC">PLC</a>|
+|**swv12**|<a href="#SWV">SWV</a>|50|10|**2972**|<a href="#V1">V1</a>|**2977**|<a href="#PLC">PLC</a>|**6097.35**|<a href="#PLC">PLC</a>|
+|swv13|<a href="#SWV">SWV</a>|50|10|3104|<a href="#V1">V1</a>|3104|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|swv14|<a href="#SWV">SWV</a>|50|10|2968|<a href="#BV">BV</a>|2968|<a href="#H">H</a>|422.81|<a href="#PLC">PLC</a>|
+|swv15|<a href="#SWV">SWV</a>|50|10|2885|<a href="#V1">V1</a>|2885|<a href="#PLC">PLC</a>|6000.57|<a href="#PLC">PLC</a>|
+|swv16|<a href="#SWV">SWV</a>|50|10|2924|<a href="#SWV">SWV</a>|2924|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|swv17|<a href="#SWV">SWV</a>|50|10|2794|<a href="#SWV">SWV</a>|2794|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|swv18|<a href="#SWV">SWV</a>|50|10|2852|<a href="#SWV">SWV</a>|2852|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|swv19|<a href="#SWV">SWV</a>|50|10|2843|<a href="#SWV">SWV</a>|2843|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|swv20|<a href="#SWV">SWV</a>|50|10|2823|<a href="#SWV">SWV</a>|2823|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|ta01|<a href="#T">T</a>|15|15|1231|<a href="#T">T</a>|1231|<a href="#H">H</a>|2.93|<a href="#PLC">PLC</a>|
+|ta02|<a href="#T">T</a>|15|15|1244|<a href="#V">V</a>|1244|<a href="#NS">NS</a>|38.09|<a href="#PLC">PLC</a>|
+|ta03|<a href="#T">T</a>|15|15|1218|<a href="#BB">BB</a>|1218|<a href="#H">H</a>|43.66|<a href="#PLC">PLC</a>|
+|ta04|<a href="#T">T</a>|15|15|1175|<a href="#BB">BB</a>|1175|<a href="#PM">PM</a>|38.72|<a href="#PLC">PLC</a>|
+|ta05|<a href="#T">T</a>|15|15|1224|<a href="#BB">BB</a>|1224|<a href="#H">H</a>|11.24|<a href="#PLC">PLC</a>|
+|ta06|<a href="#T">T</a>|15|15|1238|<a href="#BB">BB</a>|1238|<a href="#H">H</a>|178.06|<a href="#PLC">PLC</a>|
+|ta07|<a href="#T">T</a>|15|15|1227|<a href="#BB">BB</a>|1227|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|ta08|<a href="#T">T</a>|15|15|1217|<a href="#BB">BB</a>|1217|<a href="#H">H</a>|2.43|<a href="#PLC">PLC</a>|
+|ta09|<a href="#T">T</a>|15|15|1274|<a href="#BB">BB</a>|1274|<a href="#H">H</a>|18.66|<a href="#PLC">PLC</a>|
+|ta10|<a href="#T">T</a>|15|15|1241|<a href="#V">V</a>|1241|<a href="#H">H</a>|42.25|<a href="#PLC">PLC</a>|
+|ta11|<a href="#T">T</a>|20|15|1357|<a href="#VLS">VLS</a>|1357|<a href="#BFW">BFW</a>|186.19|<a href="#PLC">PLC</a>|
+|ta12|<a href="#T">T</a>|20|15|1367|<a href="#VLS">VLS</a>|1367|<a href="#H">H</a>|206.06|<a href="#PLC">PLC</a>|
+|ta13|<a href="#T">T</a>|20|15|1342|<a href="#VLS">VLS</a>|1342|<a href="#H">H</a>|161.37|<a href="#PLC">PLC</a>|
+|ta14|<a href="#T">T</a>|20|15|1345|<a href="#V">V</a>|1345|<a href="#NS">NS</a>|6|<a href="#SS">SS</a>|
+|ta15|<a href="#T">T</a>|20|15|1339|<a href="#VLS">VLS</a>|1339|<a href="#PSV">PSV</a>|173.45|<a href="#PLC">PLC</a>|
+|ta16|<a href="#T">T</a>|20|15|1360|<a href="#VLS">VLS</a>|1360|<a href="#H">H</a>|63.41|<a href="#PLC">PLC</a>|
+|ta17|<a href="#T">T</a>|20|15|1462|<a href="#S">S</a>|1462|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|**ta18**|<a href="#T">T</a>|20|15|**1377**|<a href="#VLS">VLS</a>|**1396**|<a href="#H">H</a>|**91.13**|<a href="#PLC">PLC</a>|
+|ta19|<a href="#T">T</a>|20|15|1332|<a href="#VLS">VLS</a>|1332|<a href="#PSV">PSV</a>|145.42|<a href="#PLC">PLC</a>|
+|ta20|<a href="#T">T</a>|20|15|1348|<a href="#VLS">VLS</a>|1348|<a href="#PSV">PSV</a>|216.72|<a href="#PLC">PLC</a>|
+|ta21|<a href="#T">T</a>|20|20|1642|<a href="#VLS">VLS</a>|1642|<a href="#BFW">BFW</a>|3600|<a href="#BFW">BFW</a>|
+|**ta22**|<a href="#T">T</a>|20|20|**1561**|<a href="#VLS">VLS</a>|**1600**|<a href="#H">H</a>|**228.9**|<a href="#PLC">PLC</a>|
+|**ta23**|<a href="#T">T</a>|20|20|**1518**|<a href="#VLS">VLS</a>|**1557**|<a href="#H">H</a>|**359.79**|<a href="#PLC">PLC</a>|
+|ta24|<a href="#T">T</a>|20|20|1644|<a href="#VLS">VLS</a>|1644|<a href="#VLS2">VLS2</a>|30000|<a href="#VLS2">VLS2</a>|
+|**ta25**|<a href="#T">T</a>|20|20|**1558**|<a href="#VLS">VLS</a>|**1595**|<a href="#NS2">NS2</a>|**416.08**|<a href="#PLC">PLC</a>|
+|**ta26**|<a href="#T">T</a>|20|20|**1591**|<a href="#VLS">VLS</a>|**1643**|<a href="#GR">GR</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta27**|<a href="#T">T</a>|20|20|**1652**|<a href="#VLS">VLS</a>|**1680**|<a href="#H">H</a>|**254.74**|<a href="#PLC">PLC</a>|
+|ta28|<a href="#T">T</a>|20|20|1603|<a href="#VLS">VLS</a>|1603|<a href="#PSV">PSV</a>|1514|<a href="#SS">SS</a>|
+|**ta29**|<a href="#T">T</a>|20|20|**1573**|<a href="#VLS">VLS</a>|**1625**|<a href="#H">H</a>|**93.53**|<a href="#PLC">PLC</a>|
+|**ta30**|<a href="#T">T</a>|20|20|**1519**|<a href="#VLS">VLS</a>|**1584**|<a href="#H">H</a>|**388.66**|<a href="#PLC">PLC</a>|
+|ta31|<a href="#T">T</a>|30|15|1764|<a href="#T">T</a>|1764|<a href="#H">H</a>|6|<a href="#SS">SS</a>|
+|**ta32**|<a href="#T">T</a>|30|15|**1774**|<a href="#T">T</a>|**1784**|<a href="#S2">S2</a>|||
+|**ta33**|<a href="#T">T</a>|30|15|**1788**|<a href="#VLS">VLS</a>|**1791**|<a href="#PSV">PSV</a>|**457.55**|<a href="#PLC">PLC</a>|
+|**ta34**|<a href="#T">T</a>|30|15|**1828**|<a href="#T">T</a>|**1829**|<a href="#H">H</a>|**315.71**|<a href="#PLC">PLC</a>|
+|ta35|<a href="#T">T</a>|30|15|2007|<a href="#V">V</a>|2007|<a href="#PM">PM</a>|0.56|<a href="#PLC">PLC</a>|
+|ta36|<a href="#T">T</a>|30|15|1819|<a href="#V">V</a>|1819|<a href="#H">H</a>|15|<a href="#SS">SS</a>|
+|ta37|<a href="#T">T</a>|30|15|1771|<a href="#T">T</a>|1771|<a href="#GR">GR</a>|652.24|<a href="#PLC">PLC</a>|
+|ta38|<a href="#T">T</a>|30|15|1673|<a href="#T">T</a>|1673|<a href="#H">H</a>|45|<a href="#SS">SS</a>|
+|ta39|<a href="#T">T</a>|30|15|1795|<a href="#V">V</a>|1795|<a href="#H">H</a>|6|<a href="#SS">SS</a>|
+|**ta40**|<a href="#T">T</a>|30|15|**1651**|<a href="#VLS">VLS</a>|**1669**|<a href="#GR">GR</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta41**|<a href="#T">T</a>|30|20|**1906**|<a href="#VLS">VLS</a>|**2005**|<a href="#VLS2">VLS2</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta42**|<a href="#T">T</a>|30|20|**1884**|<a href="#VLS">VLS</a>|**1937**|<a href="#GR">GR</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta43**|<a href="#T">T</a>|30|20|**1809**|<a href="#V">V</a>|**1846**|<a href="#PLC">PLC</a>|**1726.78**|<a href="#PLC">PLC</a>|
+|**ta44**|<a href="#T">T</a>|30|20|**1948**|<a href="#VLS">VLS</a>|**1979**|<a href="#VLS2">VLS2</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta45**|<a href="#T">T</a>|30|20|**1997**|<a href="#V">V</a>|**2000**|<a href="#H">H</a>|**1057.79**|<a href="#PLC">PLC</a>|
+|**ta46**|<a href="#T">T</a>|30|20|**1957**|<a href="#VLS">VLS</a>|**2004**|<a href="#GR">GR</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta47**|<a href="#T">T</a>|30|20|**1807**|<a href="#VLS">VLS</a>|**1889**|<a href="#PLC">PLC</a>, <a href="#VLS2">VLS2</a>|**1030.88**|<a href="#PLC">PLC</a>|
+|**ta48**|<a href="#T">T</a>|30|20|**1912**|<a href="#V">V</a>|**1937**|<a href="#SS">SS</a>|**3008**|<a href="#SS">SS</a>|
+|**ta49**|<a href="#T">T</a>|30|20|**1931**|<a href="#VLS">VLS</a>|**1961**|<a href="#VLS2">VLS2</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta50**|<a href="#T">T</a>|30|20|**1833**|<a href="#VLS">VLS</a>|**1923**|<a href="#PLC">PLC</a>, <a href="#VLS2">VLS2</a>|**1318.05**|<a href="#PLC">PLC</a>|
+|ta51|<a href="#T">T</a>|50|15|2760|<a href="#T">T</a>|2760|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta52|<a href="#T">T</a>|50|15|2756|<a href="#T">T</a>|2756|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta53|<a href="#T">T</a>|50|15|2717|<a href="#T">T</a>|2717|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta54|<a href="#T">T</a>|50|15|2839|<a href="#T">T</a>|2839|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta55|<a href="#T">T</a>|50|15|2679|<a href="#T">T</a>|2679|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta56|<a href="#T">T</a>|50|15|2781|<a href="#T">T</a>|2781|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta57|<a href="#T">T</a>|50|15|2943|<a href="#T">T</a>|2943|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta58|<a href="#T">T</a>|50|15|2885|<a href="#T">T</a>|2885|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta59|<a href="#T">T</a>|50|15|2655|<a href="#T">T</a>|2655|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta60|<a href="#T">T</a>|50|15|2723|<a href="#T">T</a>|2723|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta61|<a href="#T">T</a>|50|20|2868|<a href="#T">T</a>|2868|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta62|<a href="#T">T</a>|50|20|2869|<a href="#V">V</a>|2869|<a href="#C">C</a>|||
+|ta63|<a href="#T">T</a>|50|20|2755|<a href="#T">T</a>|2755|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta64|<a href="#T">T</a>|50|20|2702|<a href="#BV">BV</a>|2702|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta65|<a href="#T">T</a>|50|20|2725|<a href="#T">T</a>|2725|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta66|<a href="#T">T</a>|50|20|2845|<a href="#T">T</a>|2845|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta67|<a href="#T">T</a>|50|20|2825|<a href="#V">V</a>|2825|<a href="#H">H</a>|2000|<a href="#H">H</a>|
+|ta68|<a href="#T">T</a>|50|20|2784|<a href="#BV">BV</a>|2784|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta69|<a href="#T">T</a>|50|20|3071|<a href="#T">T</a>|3071|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta70|<a href="#T">T</a>|50|20|2995|<a href="#T">T</a>|2995|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta71|<a href="#T">T</a>|100|20|5464|<a href="#T">T</a>|5464|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta72|<a href="#T">T</a>|100|20|5181|<a href="#T">T</a>|5181|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta73|<a href="#T">T</a>|100|20|5568|<a href="#T">T</a>|5568|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta74|<a href="#T">T</a>|100|20|5339|<a href="#T">T</a>|5339|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta75|<a href="#T">T</a>|100|20|5392|<a href="#T">T</a>|5392|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta76|<a href="#T">T</a>|100|20|5342|<a href="#T">T</a>|5342|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta77|<a href="#T">T</a>|100|20|5436|<a href="#T">T</a>|5436|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta78|<a href="#T">T</a>|100|20|5394|<a href="#T">T</a>|5394|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta79|<a href="#T">T</a>|100|20|5358|<a href="#T">T</a>|5358|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta80|<a href="#T">T</a>|100|20|5183|<a href="#T">T</a>|5183|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|yn1|<a href="#YN">YN</a>|20|20|884|<a href="#KNF">KNF</a>|884|<a href="#ZSR">ZSR</a>|169.29|<a href="#PLC">PLC</a>|
+|**yn2**|<a href="#YN">YN</a>|20|20|**870**|<a href="#BB">BB</a>|**904**|<a href="#GR">GR</a>|**202.22**|<a href="#PLC">PLC</a>|
+|**yn3**|<a href="#YN">YN</a>|20|20|**859**|<a href="#VLS">VLS</a>|**892**|<a href="#NS2">NS2</a>|**344.15**|<a href="#PLC">PLC</a>|
+|**yn4**|<a href="#YN">YN</a>|20|20|**929**|<a href="#VLS">VLS</a>|**968**|<a href="#H">H</a>|**320.51**|<a href="#PLC">PLC</a>|
+
+## Literature Sources
+
+The data in this study has been taken from the following literature sources.
+We used <http://jobshop.jjvh.nl> as starting point for the search, but included additional papers.
+You can find the full BibTeX entries for the below references in our [bibliography](https://raw.githubusercontent.com/thomasWeise/jsspInstancesAndResults/master/data-raw/bibliography/bibliography.bib).
+The bibliography keys there will start with the same mnemonic as used here, but here we shortened these keys for the sake of brevity.
+
+<dl>
+<dt id="A">A</dt><dd>Abdelmaguid TF (2010). â€œRepresentations in Genetic Algorithm for the Job Shop Scheduling Problem: A Computational Study.â€ Journal of Software Engineering and Applications (JSEA), 3(12), 1155-1162. doi:<a href="https://doi.org/10.4236/jsea.2010.312135">10.4236/jsea.2010.312135</a>, <a href="http://www.scirp.org/journal/paperinformation.aspx?paperid=3561">http://www.scirp.org/journal/paperinformation.aspx?paperid=3561</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC58">A2010RIGAFTJSPACS</a></dd>
+<dt id="A2">A2</dt><dd>Asadzadeh L (2015). â€œA Local Search Genetic Algorithm for the Job Shop Scheduling Problem with Intelligent Agents.â€ Computers & Industrial Engineering, 85, 376-383. doi:<a href="https://doi.org/10.1016/j.cie.2015.04.006">10.1016/j.cie.2015.04.006</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC72">A2015ALSGAFTJSSPWIA</a></li>
+<dt id="ABZ">ABZ</dt><dd>Adams J, Balas E, Zawack D (1988). â€œThe Shifting Bottleneck Procedure for Job Shop Scheduling.â€ Management Science, 34(3), 391-401. doi:<a href="https://doi.org/10.1287/mnsc.34.3.391">10.1287/mnsc.34.3.391</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC84">ABZ1988TSBPFJSS</a></li>
+<dt id="AC">AC</dt><dd>Applegate DL, Cook WJ (1991). â€œA Computational Study of the Job-Shop Scheduling Problem.â€ ORSA Journal on Computing, 3(2), 149-156. doi:<a href="https://doi.org/10.1287/ijoc.3.2.149">10.1287/ijoc.3.2.149</a>, the JSSP instances used were generated in Bonn in 1986. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC96">AC1991ACSOTJSSP</a></li>
+<dt id="AF">AF</dt><dd>Aydin ME, Fogarty TC (2002). â€œModular Simulated Annealing for Job Shop Scheduling running on Distributed Resource Machine (DRM).â€ London South Bank University, Faculty of Business, Computing and Information Management, London, England, UK. <a href="http://www.soc.napier.ac.uk/~benp/dream/dreampaper6a.pdf">http://www.soc.napier.ac.uk/~benp/dream/dreampaper6a.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC109">AF2002MSAFJSSRODRMD</a></li>
+<dt id="AK">AK</dt><dd>Abdel-Kader RF (2018). â€œAn Improved PSO Algorithm with Genetic and Neighborhood-Based Diversity Operators for the Job Shop Scheduling Problem.â€ Applied Artificial Intelligence - An International Journal, 32(5), 433-462. doi:<a href="https://doi.org/10.1080/08839514.2018.1481903">10.1080/08839514.2018.1481903</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC119">AK2018AIPAWGANBDOFTJSSP</a></li>
+<dt id="AKZ">AKZ</dt><dd>Akram K, Kamal K, Zeb A (2016). â€œFast Simulated Annealing Hybridized with Quenching for Solving Job Shop Scheduling Problem.â€ Applied Soft Computing Journal (ASOC), 49, 510-523. doi:<a href="https://doi.org/10.1016/j.asoc.2016.08.037">10.1016/j.asoc.2016.08.037</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC132">AKZ2016FSAHWQFSJSSP</a></li>
+<dt id="AMC">AMC</dt><dd>Angel JM, MartÃnez MR, Castillo LRM, Solis LS (2014). â€œUn Modelo HÃbrido de Inteligencia Computacional para Resolver el Problema de Job Shop Scheduling.â€ Research in Computing Science, 79(Advances in Intelligent Information Technologies), 9-20. <a href="http://www.rcs.cic.ipn.mx/2014_79/RCS_79_2014.pdf">http://www.rcs.cic.ipn.mx/2014_79/RCS_79_2014.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC144">AMCS2014UMHDICPREPDJSS</a></li>
+<dt id="ASS">ASS</dt><dd>Amaria K, Souier M, Sar Z (2014). â€œArtificial Bee Colony (ABC) Algorithm for the Job-Shop Scheduling Problem.â€ In Proceedings of the 5th International Conference on Metaheuristics and Nature Inspired Computing (META'14), October 27-31, 2014, Marrakech, Morocco. The paper reports makespan 53 for ft06, which is below the lower bound of 55 and thus is not included in our dataset., <a href="https://meta2014.sciencesconf.org/42589/document">https://meta2014.sciencesconf.org/42589/document</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC157">ASS2014ABCAAFTJSSP</a></li>
+<dt id="AZ">AZ</dt><dd>Amirghasemi M, Zamani R (2015). â€œAn Effective Asexual Genetic Algorithm for Solving the Job Shop Scheduling Problem.â€ Computers & Industrial Engineering, 83, 123-138. doi:<a href="https://doi.org/10.1016/j.cie.2015.02.011">10.1016/j.cie.2015.02.011</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC166">AZ2015AEAGAFSTJSSP</a></li>
+<dt id="B">B</dt><dd>Bierwirth C (1995). â€œA Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms.â€ Operations-Research-Spektrum (OR Spectrum), 17(2-3), 87-92. doi:<a href="https://doi.org/10.1007/BF01719250">10.1007/BF01719250</a>, <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.52.7392&type=pdf">http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.52.7392&type=pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC178">B1995AGPATJSSWGA</a></li>
+<dt id="BB">BB</dt><dd>BrinkkÃ¶tter W, Brucker P (2001). â€œSolving Open Benchmark Instances for the Job-Shop Problem by Parallel Head-Tail Adjustments.â€ Journal of Scheduling, 4(1), 53-64. doi:<a href="https://doi.org/10.1002/1099-1425(200101/02">10.1002/1099-1425(200101/02)4:1<53::AID-JOS59>3.0.CO;2-Y</a>4:1<53::AID-JOS59>3.0.CO;2-Y). BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC192">BB2001SOBIFTJSPBPHTA</a></li>
+<dt id="BFW">BFW</dt><dd>Beck JC, Feng TK, Watson J (2011). â€œCombining Constraint Programming and Local Search for Job-Shop Scheduling.â€ INFORMS Journal on Computing, 23(1), 1-14. doi:<a href="https://doi.org/10.1287/ijoc.1100.0388">10.1287/ijoc.1100.0388</a>, <a href="http://cfwebprod.sandia.gov/cfdocs/CompResearch/docs/ists-sgmpcs.pdf">http://cfwebprod.sandia.gov/cfdocs/CompResearch/docs/ists-sgmpcs.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC204">BFW2011CCPALSFJSS</a></li>
+<dt id="BV">BV</dt><dd>Balas E, Vazacopoulos A (1994). â€œGuided Local Search with Shifting Bottleneck for Job Shop Scheduling.â€ Management Science Research Report MSSRâ€“609, Graduate School of Industrial Administration (GSIA), Carnegie Mellon University, Pittsburgh, PA, USA. revised November 1995. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC217">BV1994GLSWSBFJSS</a></li>
+<dt id="BV1">BV1</dt><dd>Balas E, Vazacopoulos A (1998). â€œGuided Local Search with Shifting Bottleneck for Job Shop Scheduling.â€ Management Science, 44(2), 262-275. doi:<a href="https://doi.org/10.1287/mnsc.44.2.262">10.1287/mnsc.44.2.262</a>, reports 307 as makespan for orb07, probably a typo, as the lower bound is 397. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC230">BV1998GLSWSBFJSS</a></li>
+<dt id="C">C</dt><dd>Caldeira JP (2003). â€œPrivate Communication of Result 2869 for ta62 to Ã‰ric D. Taillard, listed on Ã‰ric Taillard's Page.â€ <a href="http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/jobshop.dir/best_lb_up.txt">http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/jobshop.dir/best_lb_up.txt</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC244">C2003PCOR2FTTETLOETP</a></li>
+<dt id="CCC">CCC</dt><dd>Cruz-ChÃ¡vez MA, Cruz Rosales MH, Zavala-DÃaz JC, Aguilar JAH, RodrÄ±guez-LeÃ³ A, Avelino JCP, Orziz MEL, Salinas OH (2019). â€œHybrid Micro Genetic Multi-Population Algorithm With Collective Communication for the Job Shop Scheduling Problem.â€ IEEE Access, 7, 82358-82376. doi:<a href="https://doi.org/10.1109/ACCESS.2019.2924218">10.1109/ACCESS.2019.2924218</a>, <a href="http://ieeexplore.ieee.org/document/8743353">http://ieeexplore.ieee.org/document/8743353</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC253">CCCRZDARLAOS2019HMGMPAWCCFTJSSP</a></li>
+<dt id="CP">CP</dt><dd>Carlier J, Pinson Ã‰ (1989). â€œAn Algorithm for Solving the Job-Shop Problem.â€ Management Science, 35(2), 164-176. doi:<a href="https://doi.org/10.1287/mnsc.35.2.164">10.1287/mnsc.35.2.164</a>, jstor: 2631909. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC266">CP1989AAFSTJSP</a></li>
+<dt id="CP1">CP1</dt><dd>Carlier J, Pinson Ã‰ (1990). â€œA Practical Use of Jackson's Preemptive Schedule for Solving the Job Shop Problem.â€ Annals of Operations Research, 26(1-4), 269-287. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC279">CP1990APUOJPSFSTJSP</a></li>
+<dt id="CPL">CPL</dt><dd>Cheng TCE, Peng B, LÃ¼ Z (2016). â€œA Hybrid Evolutionary Algorithm to Solve the Job Shop Scheduling Problem.â€ Annals of Operations Research, 242(2), 223-237. doi:<a href="https://doi.org/10.1007/s10479-013-1332-5">10.1007/s10479-013-1332-5</a>, The paper reports 555 as average makespan of HEA for ft20, which is an obvious typo because the other columns have 1165, which is the lower bound. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC291">CPL2016AHEATSTJSSP</a></li>
+<dt id="DMU">DMU</dt><dd>Demirkol E, Mehta SV, Uzsoy R (1996). â€œBenchmarking for Shop Scheduling Problems.â€ Research Memorandum 96-4, School of Industrial Engineering, Purdue University, West Lafayette, IN, USA. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC305">DMU1996BFSSP</a></li>
+<dt id="DMU1">DMU1</dt><dd>Demirkol E, Mehta SV, Uzsoy R (1998). â€œBenchmarks for Shop Scheduling Problems.â€ European Journal of Operational Research (EJOR), 109(1), 137-141. doi:<a href="https://doi.org/10.1016/S0377-2217(97">10.1016/S0377-2217(97)00019-2</a>00019-2). BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC316">DMU1998BFSSP</a></li>
+<dt id="DPN">DPN</dt><dd>Dao T, Pan T, Nguyen T, Pan J (2018). â€œParallel Bat Algorithm for Optimizing Makespan in Job Shop Scheduling Problems.â€ Journal of Intelligent Manufacturing, 29(2), 451-462. doi:<a href="https://doi.org/10.1007/s10845-015-1121-x">10.1007/s10845-015-1121-x</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC329">DPNP2018PBAFOMIJSSP</a></li>
+<dt id="FGB">FGB</dt><dd>FlÃ³rez E, GÃ³mez W, Bautista L (2013). â€œAn Ant Colony Optimization Algorithm for Job Shop Scheduling Problem.â€ Computing Research Repository (CoRR) abs/1309.5110, arXiv. <a href="https://arxiv.org/pdf/1309.5110.pdf">https://arxiv.org/pdf/1309.5110.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC355">FGB2013AACOAFJSSP</a></li>
+<dt id="FT">FT</dt><dd>Fisher H, Thompson GL (1963). â€œProbabilistic Learning Combinations of Local Job-Shop Scheduling Rules.â€ In Muth JF, Thompson GL (eds.), Industrial Scheduling, 225-251. Prentice-Hall, Englewood Cliffs, NJ, USA. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC366">FT1963PLCOLJSSR</a></li>
+<dt id="FTM">FTM</dt><dd>Florian M, Trepant P, McMahon G (1971). â€œAn Implicit Enumeration Algorithm for the Machine Sequencing Problem.â€ Management Science, 17(12), B-782-B-792. doi:<a href="https://doi.org/10.1287/mnsc.17.12.B782">10.1287/mnsc.17.12.B782</a>, jstor: 2629469. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC379">FTM1971AIEAFTMSP</a></li>
+<dt id="GL">GL</dt><dd>Gharbi A, Labidi M (2010). â€œExtending the Single Machine-Based Relaxation Scheme for the Job Shop Scheduling Problem.â€ Electronic Notes in Discrete Mathematics, 36, 1057-1064. doi:<a href="https://doi.org/10.1016/j.endm.2010.05.134">10.1016/j.endm.2010.05.134</a>, this algorithm was used to solve several JSSP instances of the OR Library. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC393">GL2010ETSMBRSFTJSSP</a></li>
+<dt id="GLW">GLW</dt><dd>Gao L, Li X, Wen X, Lu C, Wen F (2015). â€œA Hybrid Algorithm based on a New Neighborhood Structure Evaluation Method for Job Shop Scheduling Problem.â€ Computers & Industrial Engineering, 88, 417-429. doi:<a href="https://doi.org/10.1016/j.cie.2015.08.002">10.1016/j.cie.2015.08.002</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC406">GLWLW2015AHABOANNSEMFSSP</a></li>
+<dt id="GR">GR</dt><dd>GonÃ§alves JF, Resende MGC (2014). â€œAn Extended Akers Graphical Method with a Biased Random-Key Genetic Algorithm for Job-Shop Scheduling.â€ International Transactions on Operational Research (ITOR), 21(2), 215-246. doi:<a href="https://doi.org/10.1111/itor.12044">10.1111/itor.12044</a>, <a href="http://mauricio.resende.info/doc/brkga-jss2011.pdf">http://mauricio.resende.info/doc/brkga-jss2011.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC418">GR2014AEAGMWABRKGAFJSS</a></li>
+<dt id="GTK">GTK</dt><dd>Gen M, Tsujimura Y, Kubota E (1994). â€œSolving Job-Shop Scheduling Problems by Genetic Algorithm.â€ In Humans, Information and Technology: Proceedings of the 1994 IEEE International Conference on Systems, Man and Cybernetics, October 2-5, 1994, San Antonio, TX, USA, volume 2. ISBN 0-7803-2129-4, doi:<a href="https://doi.org/10.1109/ICSMC.1994.400072">10.1109/ICSMC.1994.400072</a>, <a href="http://read.pudn.com/downloads151/doc/658565/00400072.pdf">http://read.pudn.com/downloads151/doc/658565/00400072.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC342">GTK1994SJSSPBGA</a></li>
+<dt id="GvH">GvH</dt><dd>Gromicho JAS, van Hoorn JJ, Saldanha-da-Gama F, Timmer GT (2009). â€œExponentially Better than Brute Force: Solving the Job-Shop Scheduling Problem Optimally by Dynamic Programming.â€ Research Memorandum 2009-56, Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. <a href="http://degree.ubvu.vu.nl/repec/vua/wpaper/pdf/20090056.pdf">http://degree.ubvu.vu.nl/repec/vua/wpaper/pdf/20090056.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC432">GvHSGT2009</a></li>
+<dt id="H">H</dt><dd>Henning A (2002). Praktische Job-Shop Scheduling-Probleme. Ph.D. thesis, Friedrich-Schiller-UniversitÃ¤t Jena, Jena, Germany. alternate url: https://nbn-resolving.org/urn:nbn:de:gbv:27-20060809-115700-4, <a href="http://www.db-thueringen.de/servlets/DocumentServlet?id=873">http://www.db-thueringen.de/servlets/DocumentServlet?id=873</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC444">H2002PJSSP</a></li>
+<dt id="HRS">HRS</dt><dd>HernÃ¡ndez-RamÃrez L, Solis JF, Castilla-Valdez G, GonzÃ¡lez-Barbosa JJ, TerÃ¡n-Villanueva D, Morales-RodrÃguez ML (2019). â€œA Hybrid Simulated Annealing for Job Shop Scheduling Problem.â€ International Journal of Combinatorial Optimization Problems and Informatics (IJCOPI), 10(1), 6-15. published 2018-08-10, <a href="http://ijcopi.org/index.php/ojs/article/view/111">http://ijcopi.org/index.php/ojs/article/view/111</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC457">HRSCVGBTVMR2019AHSAFJSSP</a></li>
+<dt id="HY">HY</dt><dd>Han B, Yang J (2020). â€œResearch on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN.â€ IEEE Access, 8, 186474-186495. doi:<a href="https://doi.org/10.1109/ACCESS.2020.3029868">10.1109/ACCESS.2020.3029868</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC471">HY2020ROAJSSPBODDD</a></li>
+<dt id="JM">JM</dt><dd>Jain AS, Meeran S (1999). â€œDeterministic Job-Shop Scheduling: Past, Present and Future.â€ European Journal of Operational Research (EJOR), 113(2), 390-434. doi:<a href="https://doi.org/10.1016/S0377-2217(98">10.1016/S0377-2217(98)00113-1</a>00113-1). BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC482">JM1999DJSSPPAF</a></li>
+<dt id="JPD">JPD</dt><dd>Jorapur V, Puranik VS, Deshpande AS, Sharma MR (2014). â€œComparative Study of Different Representations in Genetic Algorithms for Job Shop Scheduling Problem.â€ Journal of Software Engineering and Applications (JSEA), 7(7), 571-580. doi:<a href="https://doi.org/10.4236/jsea.2014.77053">10.4236/jsea.2014.77053</a>, <a href="http://www.scirp.org/journal/paperinformation.aspx?paperid=46670">http://www.scirp.org/journal/paperinformation.aspx?paperid=46670</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC495">JPDS2014CAODRIGAFJSSP</a></li>
+<dt id="JZ">JZ</dt><dd>Jiang T, Zhang C (2018). â€œApplication of Grey Wolf Optimization for Solving Combinatorial Problems: Job Shop and Flexible Job Shop Scheduling Cases.â€ IEEE Access, 6, 26231-26240. doi:<a href="https://doi.org/10.1109/ACCESS.2018.2833552">10.1109/ACCESS.2018.2833552</a>, <a href="http://ieeexplore.ieee.org/document/8355479">http://ieeexplore.ieee.org/document/8355479</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC509">JZ2018AOGWOFSCPJSAFJSSC</a></li>
+<dt id="K">K</dt><dd>Kolonko M (1999). â€œSome New Results on Simulated Annealing Applied to the Job Shop Scheduling Problem.â€ European Journal of Operational Research (EJOR), 113(1), 123-136. doi:<a href="https://doi.org/10.1016/S0377-2217(97">10.1016/S0377-2217(97)00420-7</a>00420-7). BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC522">K1999SNROSAATTJSSP</a></li>
+<dt id="K2">K2</dt><dd>Kurdi M (2015). â€œA New Hybrid Island Model Genetic Algorithm for Job Shop Scheduling Problem.â€ Computers & Industrial Engineering, 88, 273-283. doi:<a href="https://doi.org/10.1016/j.cie.2015.07.015">10.1016/j.cie.2015.07.015</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC535">K2015ANHIMGAFJSSP</a></li>
+<dt id="KNF">KNF</dt><dd>Koshimura M, Nabeshima H, Fujita H, Hasegawa R (2010). â€œSolving Open Job-Shop Scheduling Problems by SAT Encoding.â€ IEICE Transactions on Information and Systems, E93.D(8), 2316-2318. doi:<a href="https://doi.org/10.1587/transinf.E93.D.2316">10.1587/transinf.E93.D.2316</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC547">KNFH2010SOJSSPBSE</a></li>
+<dt id="KV">KV</dt><dd>Kulkarni K, Venkateswaran J (2014). â€œIterative Simulation and Optimization Approach for Job Shop Scheduling.â€ In Buckley SJ, Miller JA (eds.), Proceedings of the 2014 Winter Simulation Conference, December 7-10, 2014, Savannah, GA, USA, 1620-1631. doi:<a href="https://doi.org/10.1109/WSC.2014.7020013">10.1109/WSC.2014.7020013</a>, <a href="https://www.anylogic.com/upload/iblock/5aa/5aa2987b839049668eeef8a21c811e6b.pdf">https://www.anylogic.com/upload/iblock/5aa/5aa2987b839049668eeef8a21c811e6b.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC559">KV2014ISAOAFJSS</a></li>
+<dt id="L">L</dt><dd>Lawrence SR (1984). Resource Constrained Project Scheduling: An Experimental Investigation of Heuristic Scheduling Techniques (Supplement). Ph.D. thesis, Graduate School of Industrial Administration (GSIA), Carnegie-Mellon University, Pittsburgh, PA, USA. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC572">L1998RCPSAEIOHSTS</a></li>
+<dt id="LWF">LWF</dt><dd>Li L, Weng W, Fujimura S (2017). â€œAn Improved Teaching-Learning-based Optimization Algorithm to Solve Job Shop Scheduling Problems.â€ In Zhu G, Yao S, Cui X, Xu S (eds.), 16th IEEE/ACIS International Conference on Computer and Information Science (ICIS'17), May 24-26, 2017, Wuhan, China, 797-801. ISBN 978-1-5090-5507-4, doi:<a href="https://doi.org/10.1109/ICIS.2017.7960101">10.1109/ICIS.2017.7960101</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC582">LWF2017AITLBOATSJSSP</a></li>
+<dt id="LYL">LYL</dt><dd>Liu M, Yao X, Li Y (2020). â€œHybrid Whale Optimization Algorithm Enhanced with LÃ©vy Flight and Differential Evolution for Job Shop Scheduling Problems.â€ Applied Soft Computing Journal (ASOC), 87, 105954. doi:<a href="https://doi.org/10.1016/j.asoc.2019.105954">10.1016/j.asoc.2019.105954</a>, Originally, the paper had two typos in the results. It reports an average result (918.4) for WSO-LFDE on la20, which is worse than the worst result (902) it reports. We therefore ignore the worst reported result for that algorithm on that instance, since it was probably accidentally copy-pasted from the best result. On instance la23, the lower bound is 1032 but the result 1023 is reported, which is clearly an accidental typo. These typos are currently fixed in an erratum process. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC594">LYL2020HWOAEWLFADEFJSSP</a></li>
+<dt id="M">M</dt><dd>Martin PD (1996). A Time-Oriented Approach to Computing Optimal Schedules for the Job-Shop Scheduling Problem. Ph.D. thesis, School of Operations Research and Industrial Engineering, Cornell University, Ithaca, NY, USA. oclc: 64683112. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC606">M1996ATOATCOSFTJSSP</a></li>
+<dt id="M2">M2</dt><dd>Mahapatra DK (2012). â€œBachelor's Thesis: Job Shop Scheduling using Artificial Immune System.â€ guided by Prof. S. S. Mahapatra, <a href="http://pdfs.semanticscholar.org/a350/070a2612d046d11feb33e64d1ab58cd8870d.pdf">http://pdfs.semanticscholar.org/a350/070a2612d046d11feb33e64d1ab58cd8870d.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC617">M2012JSSUAIS</a></li>
+<dt id="MF">MF</dt><dd>McMahon G, Florian M (1975). â€œOn Scheduling with Ready Times and Due Dates to Minimize Maximum Lateness.â€ Operations Research, 23(3), 475-482. doi:<a href="https://doi.org/10.1287/opre.23.3.475">10.1287/opre.23.3.475</a>, jstor: 169697. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC629">MF1975OSWRTADDTMML</a></li>
+<dt id="MHT">MHT</dt><dd>Mui NH, Hoa VD, Tuyen LT (2012). â€œA Parallel Genetic Algorithm for the Job Shop Scheduling Problem.â€ In Proceedings of the IEEE International Symposium on Signal Processing and Information Technology (ISSPIT'12), December 12-15, 2012, Ho Chi Minh City, Vietnam, 19-24. ISBN 978-1-4673-5604-6, doi:<a href="https://doi.org/10.1109/ISSPIT.2012.6621254">10.1109/ISSPIT.2012.6621254</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC643">MHT2012APGAFTJSSP</a></li>
+<dt id="MM">MM</dt><dd>MagalhÃ£es-Mendes J (2013). â€œA Comparative Study of Crossover Operators for Genetic Algorithms to Solve the Job Shop Scheduling Problem.â€ WSEAS Transactions on Computers, 12(4), 164-173. <a href="http://www.wseas.org/multimedia/journals/computers/2013/5705-156.pdf">http://www.wseas.org/multimedia/journals/computers/2013/5705-156.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC656">MM2013ACSOCOFGATSTJSSP</a></li>
+<dt id="MNK">MNK</dt><dd>Maqsood S, Noor S, Khan MK, Wood A (2012). â€œHybrid Genetic Algorithm (GA) for Job Shop Scheduling Problems and its Sensitivity Analysis.â€ International Journal of Intelligent Systems Technologies and Applications (IJISTA), 11(1/2), 49-62. doi:<a href="https://doi.org/10.1504/IJISTA.2012.046543">10.1504/IJISTA.2012.046543</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC669">MNKW2012HGAGFJSSPAISA</a></li>
+<dt id="MTS">MTS</dt><dd>Miller-Todd J, SteinhÃ¶fel K, Veenstra P (2018). â€œFirefly-Inspired Algorithm for Job Shop Scheduling.â€ In BÃ¶ckenhauer H, Komm D, Unger W (eds.), Adventures Between Lower Bounds and Higher Altitudes - Essays Dedicated to Juraj HromkoviÄ on the Occasion of His 60th Birthday, volume 11011 series Lecture Notes in Computer Science (LNCS), 423-433. Springer. ISBN 978-3-319-98354-7, doi:<a href="https://doi.org/10.1007/978-3-319-98355-4_24">10.1007/978-3-319-98355-4_24</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC681">MTSV2018FIAFJSS</a></li>
+<dt id="N">N</dt><dd>Nazif H (2015). â€œSolving Job Shop Scheduling Problem Using an Ant Colony Algorithm.â€ Journal of Asian Scientific Research, 5(5), 261-268. doi:<a href="https://doi.org/10.18488/journal.2/2015.5.5/2.5.261.268">10.18488/journal.2/2015.5.5/2.5.261.268</a>, <a href="http://www.aessweb.com/pdf-files/jasr-2015-5(5)-261-268.pdf">http://www.aessweb.com/pdf-files/jasr-2015-5(5)-261-268.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC696">N2015SJSSPUAACO</a></li>
+<dt id="NA">NA</dt><dd>Narendhar S, Amudha T (2012). â€œA Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems.â€ International Journal of Programming Languages and Applications (IJPLA), 2(4), 1-11. doi:<a href="https://doi.org/10.5121/ijpla.2012.2401">10.5121/ijpla.2012.2401</a>, Also available via Computing Research Repository (CoRR) abs/1211.4971 at arXiv:1211.4971v1 [cs.NE], <a href="https://arxiv.org/pdf/1211.4971.pdf">https://arxiv.org/pdf/1211.4971.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC709">NA2012AHBFAFSJSSP</a></li>
+<dt id="NS">NS</dt><dd>Nowicki E, Smutnicki C (1996). â€œA Fast Taboo Search Algorithm for the Job Shop Problem.â€ Management Science, 42(6), 783-938. doi:<a href="https://doi.org/10.1287/mnsc.42.6.797">10.1287/mnsc.42.6.797</a>, jstor: 2634595, <a href="http://pacciarelli.inf.uniroma3.it/CORSI/MSP/NowickiSmutnicki96.pdf">http://pacciarelli.inf.uniroma3.it/CORSI/MSP/NowickiSmutnicki96.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC724">NS1996AFTSAFTJSP</a></li>
+<dt id="NS2">NS2</dt><dd>Nowicki E, Smutnicki C (2005). â€œAn Advanced Taboo Search Algorithm for the Job Shop Problem.â€ Journal of Scheduling, 8(2), 145-159. doi:<a href="https://doi.org/10.1007/s10951-005-6364-5">10.1007/s10951-005-6364-5</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC739">NS2005AATSAFTJSP</a></li>
+<dt id="NZJ">NZJ</dt><dd>Nguyen S, Zhang M, Johnston M, Tan KC (2013). â€œA Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem.â€ IEEE Transactions on Evolutionary Computation (TEVC), 17(5), 621-639. doi:<a href="https://doi.org/10.1109/TEVC.2012.2227326">10.1109/TEVC.2012.2227326</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC752">NZJT2013ACSORIGPTED</a></li>
+<dt id="ODP">ODP</dt><dd>Oliveira JA, Dias L, Pereira G (2010). â€œSolving the Job Shop Problem with a Random Keys Genetic Algorithm with Instance Parameters.â€ In Rodrigues H, Herskovits J, Soares CM, Guedes JM, Folgado J, AraÃºjo A, Moleiro F, Kuzhichalil JP, Madeira JA, DimitrovovÃ¡ Z (eds.), Proceedings of the 2nd International Conference on Engineering Optimization (EngOpt2010), September 6-9, 2010, Lisbon, Portugal. ISBN 978-989-96264-3-0, <a href="http://www1.dem.ist.utl.pt/engopt2010/Book_and_CD/Papers_CD_Final_Version/pdf/08/01512-01.pdf">http://www1.dem.ist.utl.pt/engopt2010/Book_and_CD/Papers_CD_Final_Version/pdf/08/01512-01.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC765">ODP2010STJSPWARKGAWIP</a></li>
+<dt id="OV">OV</dt><dd>Ombuki BM, Ventresca M (2004). â€œLocal Search Genetic Algorithms for the Job Shop Scheduling Problem.â€ Applied Intelligence - The International Journal of Research on Intelligent Systems for Real Life Complex Problems, 21(1), 99-109. doi:<a href="https://doi.org/10.1023/B:APIN.0000027769.48098.91">10.1023/B:APIN.0000027769.48098.91</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC779">OV2004LSGAFTJSSP</a></li>
+<dt id="P">P</dt><dd>Pongchairerks P (2014). â€œVariable Neighbourhood Search Algorithms Applied to Job-Shop Scheduling Problems.â€ International Journal of Mathematics in Operational Research (IJMOR), 6(6), 752-774. doi:<a href="https://doi.org/10.1504/IJMOR.2014.065421">10.1504/IJMOR.2014.065421</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC792">P2014VNSAATJSSP</a></li>
+<dt id="P2">P2</dt><dd>Pongchairerks P (2019). â€œA Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem.â€ Complexity, 2019(8683472), 1-11. doi:<a href="https://doi.org/10.1155/2019/8683472">10.1155/2019/8683472</a>, <a href="http://www.hindawi.com/journals/complexity/2019/8683472/">http://www.hindawi.com/journals/complexity/2019/8683472/</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC804">P2019ATLMAFTJSSP</a></li>
+<dt id="PLC">PLC</dt><dd>Peng B, LÃ¼ Z, Cheng TCE (2015). â€œA Tabu Search/Path Relinking Algorithm to Solve the Job Shop Scheduling Problem.â€ Computers & Operations Research, 53, 154-164. doi:<a href="https://doi.org/10.1016/j.cor.2014.08.006">10.1016/j.cor.2014.08.006</a>, A February 2014 preprint is available as arXiv:1402.5613v1 [cs.DS], <a href="http://arxiv.org/abs/1402.5613">http://arxiv.org/abs/1402.5613</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC817">PLC2015ATSPRATSTJSSP</a></li>
+<dt id="PM">PM</dt><dd>Pezzella F, Merelli E (2000). â€œA Tabu Search Method Guided by Shifting Bottleneck for the Job Shop Scheduling Problem.â€ European Journal of Operational Research (EJOR), 120(2), 297-310. doi:<a href="https://doi.org/10.1016/S0377-2217(99">10.1016/S0377-2217(99)00158-7</a>00158-7), <a href="https://www2.cs.sfu.ca/CourseCentral/827/havens/papers/topic%2310(JobShop)/Tabu%20With%20Shifting.pdf">https://www2.cs.sfu.ca/CourseCentral/827/havens/papers/topic%2310(JobShop)/Tabu%20With%20Shifting.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC831">PM2000ATSMGBSBFTJSSP</a></li>
+<dt id="PPH">PPH</dt><dd>PÃ©rez E, Posada M, Herrera F (2012). â€œAnalysis of New Niching Genetic Algorithms for Finding Multiple Solutions in the Job Shop Scheduling.â€ Journal of Intelligent Manufacturing, 23(3), 341-356. doi:<a href="https://doi.org/10.1007/s10845-010-0385-4">10.1007/s10845-010-0385-4</a>, reports result 595.97 for la03, which is below the lower bound of 597 and thus not included in our data set. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC845">PPH2012AONNGAFMSITJSS</a></li>
+<dt id="PSV">PSV</dt><dd>Pardalos PM, Shylo OV, Vazacopoulos A (2010). â€œSolving Job Shop Scheduling Problems Utilizing the Properties of Backbone and "Big Valley".â€ Computational Optimization and Applications, 47(1), 61-76. doi:<a href="https://doi.org/10.1007/s10589-008-9206-5">10.1007/s10589-008-9206-5</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC859">PSV2010SJSSPUTPOBABV</a></li>
+<dt id="QL">QL</dt><dd>Qiu X, Lau HYK (2014). â€œAn AIS-based Hybrid Algorithm for Static Job Shop Scheduling Problem.â€ Journal of Intelligent Manufacturing, 25(3), 489-503. doi:<a href="https://doi.org/10.1007/s10845-012-0701-2">10.1007/s10845-012-0701-2</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC872">QL2014AABHAFSSSP</a></li>
+<dt id="RNK">RNK</dt><dd>Raeesi N. MR, Kobti Z (2012). â€œA Knowledge-Migration-Based Multi-Population Cultural Algorithm to Solve Job Shop Scheduling.â€ In Youngblood GM, McCarthy PM (eds.), Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference (FLAIRS'12), May 23-25, 2012, Marco Island, FL, USA. ISBN 978-1-57735-558-8, <a href="http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS12/paper/view/4378/4768">http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS12/paper/view/4378/4768</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC885">RNK2012AKMBMPCATSJSS</a></li>
+<dt id="S">S</dt><dd>Schilham R (2000). â€œResults listed on Ã‰ric Taillard's Page.â€ see also http://jobshop.jjvh.nl/, <a href="http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html">http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC907">S200RLOETP</a></li>
+<dt id="S2">S2</dt><dd>Shylo OV (2019). â€œJob Shop Scheduling (Personal Homepage).â€ <a href="http://optimizizer.com/jobshop.php">http://optimizizer.com/jobshop.php</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC897">S2019JSSPH</a></li>
+<dt id="SB">SB</dt><dd>SabuncuoÄŸlu Ä°, Bayiz M (1999). â€œJob Shop Scheduling with Beam Search.â€ European Journal of Operational Research (EJOR), 118(2), 390-412. doi:<a href="https://doi.org/10.1016/S0377-2217(98">10.1016/S0377-2217(98)00319-1</a>00319-1), <a href="http://yoksis.bilkent.edu.tr/doi_getpdf/articles/10.1016-S0377-2217(98)00319-1.pdf">http://yoksis.bilkent.edu.tr/doi_getpdf/articles/10.1016-S0377-2217(98)00319-1.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC916">SB1999JSSWBS</a></li>
+<dt id="SIS">SIS</dt><dd>Shi G, Iima H, Sannomiya N (1997). â€œNew Encoding Scheme for Solving Job Shop Problems by Genetic Algorithm.â€ In Proceedings of the 35th IEEE Conference on Decision and Control (CDC'96), December 11-13, 1996, Kobe, Japan, volume 4, 4395-4400. ISBN 0-7803-3590-2, doi:<a href="https://doi.org/10.1109/CDC.1996.577484">10.1109/CDC.1996.577484</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC930">SIS1997NESFSJSPBGA</a></li>
+<dt id="SK">SK</dt><dd>Sakuma J, Kobayashi S (2000). â€œExtrapolation-Directed Crossover for Job-Shop Scheduling Problems: Complementary Combination with JOX.â€ In Whitley LD, Goldberg DE, CantÃº-Paz E, Spector L, Parmee IC, Beyer H (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'00), July 8-12, 2000, Las Vegas, NV, USA, 973-980. ISBN 1-55860-708-0. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC943">SK2000EDCFJSSPCCWJ</a></li>
+<dt id="SMM">SMM</dt><dd>Sahana SK, Mukherjee I, Mahanti PK (2018). â€œParallel Artificial Bee Colony (PABC) for Job Shop Scheduling Problems.â€ Advances in Information Sciences and Service Sciences (AISS), 10(3), 1-11. reports 661 as result for abz9 which is below the lower bound 678 and thus not included in our data set, <a href="http://www.globalcis.org/aiss/ppl/AISS3877PPL.pdf">http://www.globalcis.org/aiss/ppl/AISS3877PPL.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC955">SMM2018PABCPFJSSP</a></li>
+<dt id="SS">SS</dt><dd>Shylo OV, Shams H (2018). â€œBoosting Binary Optimization via Binary Classification: A Case Study of Job Shop Scheduling.â€ cs.AI/math.OC abs/1808.10813, arXiv. Many results are available in the GitHub repository https://github.com/quasiquasar/gta-jobshop-data. We just use a subset (namely, samples after 3, 5, 30, and 60 minutes, and the end results) to compute statistics. The paper reports some new bks for which the creating runs are not contained in the GitHub repository, verified via email with the authors, as well as bound 6196 for both dmu74 and dmu75. Other results have been published on Prof. Shylo's website http://optimizizer.com/DMU.php for the same paper (including dmu17), <a href="https://arxiv.org/pdf/1808.10813">https://arxiv.org/pdf/1808.10813</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC969">SS2018BBOVBCACSOJSS</a></li>
+<dt id="SSS">SSS</dt><dd>Sharma N, Sharma H, Sharma A (2018). â€œBeer Froth Artificial Bee Colony Algorithm for Job-Shop Scheduling Problem.â€ Applied Soft Computing Journal (ASOC), 68, 507-524. doi:<a href="https://doi.org/10.1016/j.asoc.2018.04.001">10.1016/j.asoc.2018.04.001</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC982">SSS2018BFABCAFJSSP</a></li>
+<dt id="SWV">SWV</dt><dd>Storer RH, Wu SD, Vaccari R (1992). â€œNew Search Spaces for Sequencing Problems with Application to Job Shop Scheduling.â€ Management Science, 38(10), 1495-1509. doi:<a href="https://doi.org/10.1287/mnsc.38.10.1495">10.1287/mnsc.38.10.1495</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC993">SWV1992NSSFSPWATJSS</a></li>
+<dt id="T">T</dt><dd>Taillard Ã‰D (1993). â€œBenchmarks for Basic Scheduling Problems.â€ European Journal of Operational Research (EJOR), 64(2), 278-285. doi:<a href="https://doi.org/10.1016/0377-2217(93">10.1016/0377-2217(93)90182-M</a>90182-M). BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1005">T199BFBSP</a></li>
+<dt id="V">V</dt><dd>Vaessens RJM (1995). â€œResults listed on Ã‰ric Taillard's Page.â€ see also http://jobshop.jjvh.nl/, <a href="http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html">http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1027">V1995RLOETP</a></li>
+<dt id="V1">V1</dt><dd>Vaessens RJM (1996). â€œAddition to John Edward Beasley's OR Library.â€ see also http://jobshop.jjvh.nl/, <a href="http://people.brunel.ac.uk/~mastjjb/jeb/orlib/files/jobshop1.txt">http://people.brunel.ac.uk/~mastjjb/jeb/orlib/files/jobshop1.txt</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1018">V1996ATJEBOL</a></li>
+<dt id="VAL">VAL</dt><dd>Vaessens RJM, Aarts EHL, Lenstra JK (1996). â€œJob Shop Scheduling by Local Search.â€ INFORMS Journal on Computing, 8(3), 302-317. doi:<a href="https://doi.org/10.1287/ijoc.8.3.302">10.1287/ijoc.8.3.302</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1036">VAL1996JSSBLS</a></li>
+<dt id="vH">vH</dt><dd>van Hoorn JJ (2015). â€œJob Shop Instances and Solutions.â€ <a href="http://jobshop.jjvh.nl">http://jobshop.jjvh.nl</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1049">vH2015JSIAS</a></li>
+<dt id="vH2">vH2</dt><dd>van Hoorn JJ (2016). Dynamic Programming for Routing and Scheduling: Optimizing Sequences of Decisions. Ph.D. thesis, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. <a href="http://jobshop.jjvh.nl/dissertation">http://jobshop.jjvh.nl/dissertation</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1057">vH2016DPFRASOSOD</a></li>
+<dt id="VLS">VLS</dt><dd>VilÃm P, Laborie P, Shaw P (2015). â€œFailure-Directed Search for Constraint-Based Scheduling.â€ In Michel L (ed.), International Conference Integration of AI and OR Techniques in Constraint Programming: Proceedings of 12th International Conference on AI and OR Techniques in Constriant Programming for Combinatorial Optimization Problems (CPAIOR'2015), May 18-22, 2015, Barcelona, Spain, volume 9075 series Lecture Notes in Computer Science (LNCS) and Theoretical Computer Science and General Issues book sub series (LNTCS), 437-453. ISBN 978-3-319-18007-6, doi:<a href="https://doi.org/10.1007/978-3-319-18008-3_30">10.1007/978-3-319-18008-3_30</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1070">VLS2015FDSFCBS</a></li>
+<dt id="VLS2">VLS2</dt><dd>VilÃm P, Laborie P, Shaw P (2015). â€œFailure-Directed Search for Constraint-Based Scheduling - Detailed Experimental Results.â€ The detailed experimental results of the paper "Failure-Directed Search for Constraint-Based Scheduling" by the same authors, in International Conference Integration of AI and OR Techniques in Constraint Programming: Proceedings of 12th International Conference on AI and OR Techniques in Constriant Programming for Combinatorial Optimization Problems (CPAIOR'2015), May 18-22, 2015, Barcelona, Spain, pages 437-453, doi:10.1007/978-3-319-18008-3_30., <a href="http://vilim.eu/petr/cpaior2015-results.pdf">http://vilim.eu/petr/cpaior2015-results.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1086">VLS2015FDSFCBSDER</a></li>
+<dt id="W">W</dt><dd>Weise T (2019-2020). â€œjsspInstancesAndResults: Results, Data, and Instances of the Job Shop Scheduling Problem.â€ A GitHub repository with the common benchmark instances for the Job Shop Scheduling Problem as well as results from the literature, both in form of CSV files as well as R program code to access them., <a href="https://github.com/thomasWeise/jsspInstancesAndResults">https://github.com/thomasWeise/jsspInstancesAndResults</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1096">W2019JRDAIOTJSSP</a></li>
+<dt id="WCL">WCL</dt><dd>Wang L, Cai J, Li M (2016). â€œAn Adaptive Multi-Population Genetic Algorithm for Job-Shop Scheduling Problem.â€ Advances in Manufacturing, 4(2), 142-149. doi:<a href="https://doi.org/10.1007/s40436-016-0140-y">10.1007/s40436-016-0140-y</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1107">WCL2016AAMPGAFJSSP</a></li>
+<dt id="WD">WD</dt><dd>Wang X, Duan H (2014). â€œA Hybrid Biogeography-based Optimization Algorithm for Job Shop Scheduling Problem.â€ Computers & Industrial Engineering, 73, 96-114. doi:<a href="https://doi.org/10.1016/j.cie.2014.04.006">10.1016/j.cie.2014.04.006</a>, <a href="http://hbduan.buaa.edu.cn/papers/2014CAIE_Wang_Duan.pdf">http://hbduan.buaa.edu.cn/papers/2014CAIE_Wang_Duan.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1120">WD2014AHBBOAFJSSP</a></li>
+<dt id="WGK">WGK</dt><dd>Weckman GR, Ganduri CV, Koonce DA (2008). â€œA Neural Network Job-Shop Scheduler.â€ Journal of Intelligent Manufacturing, 19, 191-201. doi:<a href="https://doi.org/10.1007/s10845-008-0073-9">10.1007/s10845-008-0073-9</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1133">WGK2008ANNJSS</a></li>
+<dt id="WTC">WTC</dt><dd>Wang S, Tsai C, Chiang M (2018). â€œA High Performance Search Algorithm for Job-Shop Scheduling Problem.â€ In Shakshuki EM, Yasar A (eds.), The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN'18) / The 8th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH'18) / Affiliated Workshops, November 5-8, 2018, Leuven, Belgium, volume 141 series Procedia Computer Science, 119-126. doi:<a href="https://doi.org/10.1016/j.procs.2018.10.157">10.1016/j.procs.2018.10.157</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1146">WTC2018AHPSAFJSSP</a></li>
+<dt id="YN">YN</dt><dd>Yamada T, Nakano R (1992). â€œA Genetic Algorithm Applicable to Large-Scale Job-Shop Instances.â€ In MÃ¤nner R, Manderick B (eds.), Proceedings of Parallel Problem Solving from Nature 2 (PPSN II), September 28-30, 1992, Brussels, Belgium, 281-290. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1160">YN1992AGAATLSJSI</a></li>
+<dt id="YN1">YN1</dt><dd>Yamada T, Nakano R (1997). â€œGenetic Algorithms for Job-Shop Scheduling Problems.â€ In Proceedings of Modern Heuristic for Decision Support, March18-19, 1997, London, England, UK, 67-81. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1172">YN1997GAFJSSP</a></li>
+<dt id="ZHZ">ZHZ</dt><dd>Zupan H, HerakoviÄ N, Å½erovnik J (2016). â€œA Heuristic for the Job Shop Scheduling Problem.â€ In Papa G, Mernik M (eds.), The 7th International Conference on Bioinspired Optimization Methods and their Application (BIOMA'16), May 18-20, 2016, Bled, Slovenia, 187-198. ISBN 978-961-264-093-4, <a href="http://bioma.ijs.si/conference/BIOMA2016Proceedings.pdf">http://bioma.ijs.si/conference/BIOMA2016Proceedings.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1182">ZHZ2016AHFTJSSP</a></li>
+<dt id="ZLR">ZLR</dt><dd>Zhang C, Li P, Rao Y, Guan Z (2008). â€œA Very Fast TS/SA Algorithm for the Job Shop Scheduling Problem.â€ Computers & Operations Research, 35(1), 282-294. doi:<a href="https://doi.org/10.1016/j.cor.2006.02.024">10.1016/j.cor.2006.02.024</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1197">ZLRG2008AVFTAFTJSSP</a></li>
+<dt id="ZRL">ZRL</dt><dd>Zhang C, Rao Y, Li P (2008). â€œAn Effective Hybrid Genetic Algorithm for the Job Shop Scheduling Problem.â€ International Journal of Advanced Manufacturing Technology (JAMT), 39, 965-974. doi:<a href="https://doi.org/10.1007/s00170-007-1354-8">10.1007/s00170-007-1354-8</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1210">ZRL2008AEHGAFTJSSP</a></li>
+<dt id="ZSR">ZSR</dt><dd>Zhang C, Shao X, Rao Y, Qiu H (2008). â€œSome New Results on Tabu Search Algorithm Applied to the Job-Shop Scheduling Problem.â€ In Jaziri W (ed.), Tabu Search. IntechOpen, London, England, UK. ISBN 978-3-902613-34-9, doi:<a href="https://doi.org/10.5772/5593">10.5772/5593</a>, <a href="http://www.intechopen.com/books/tabu_search/some_new_results_on_tabu_search_algorithm_applied_to_the_job-shop_scheduling_problem">http://www.intechopen.com/books/tabu_search/some_new_results_on_tabu_search_algorithm_applied_to_the_job-shop_scheduling_problem</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1222">ZSRQ2008SNROTSAATTJSSP</a></li>
+</dl>
+
 ## Development Setup
 
 If you want to check out the code and implement new features or fix bugs, you can set up the project as follows:
@@ -214,7 +584,6 @@ This project features most of the extensions featured in this Tutorial: [Documen
 If you have any questions or feedback, feel free to contact me via [email](mailto:alexander.nasuta@wzl-iqs.rwth-aachen.de) or open an issue on repository.
 
 
-
 # License
 
 Distributed under the MIT License. See `LICENSE.txt` for more information.
diff --git a/docs/source/_static/ft06-std.txt b/docs/source/_static/ft06-std.txt
new file mode 100644
index 0000000..0cc2130
--- /dev/null
+++ b/docs/source/_static/ft06-std.txt
@@ -0,0 +1,7 @@
+6	6 
+2	1	0	3	1	6	3	7	5	3	4	6
+1	8	2	5	4	10	5	10	0	10	3	4
+2	5	3	4	5	8	0	9	1	1	4	7
+1	5	0	5	2	5	3	3	4	8	5	9
+2	9	1	3	4	5	5	4	0	3	3	1
+1	3	3	3	5	9	0	10	4	4	2	1
diff --git a/docs/source/_static/ft06-ta.txt b/docs/source/_static/ft06-ta.txt
new file mode 100644
index 0000000..7d026eb
--- /dev/null
+++ b/docs/source/_static/ft06-ta.txt
@@ -0,0 +1,13 @@
+6	6
+1	3	6	7	3	6
+8	5	10	10	10	4
+5	4	8	9	1	7
+5	5	5	3	8	9
+9	3	5	4	3	1
+3	3	9	10	4	1
+3	1	2	4	6	5
+2	3	5	6	1	4
+3	4	6	1	2	5
+2	1	3	4	5	6
+3	2	5	6	1	4
+2	4	6	1	5	3
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 5b1e595..8f0dc56 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -16,6 +16,7 @@ release = '1.0.2'
 
 extensions = [
     "myst_parser",
+
     "sphinx.ext.duration",
     "sphinx_copybutton",
 
@@ -27,8 +28,11 @@ extensions = [
     "sphinx.ext.autodoc",
 
     "sphinx.ext.napoleon",
+
+    "nbsphinx",
 ]
 
+
 templates_path = ['_templates']
 exclude_patterns = []
 
@@ -36,7 +40,7 @@ exclude_patterns = []
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
 html_theme = 'furo'
-html_title = f"Graph Job Shop Environment"
+html_title = f"JSP Benchmark Instances"
 html_static_path = ['_static']
 html_theme_options = {
     "light_logo": "logo-light.png",
diff --git a/docs/source/examples/JSSEnv_example.ipynb b/docs/source/examples/JSSEnv_example.ipynb
new file mode 100644
index 0000000..ab9112d
--- /dev/null
+++ b/docs/source/examples/JSSEnv_example.ipynb
@@ -0,0 +1,180 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# JSSEnv Example"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "87684e89c5a41617"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:11.519106Z",
+     "start_time": "2025-03-20T14:07:11.516207Z"
+    }
+   },
+   "id": "d50995faa4b1bbeb",
+   "execution_count": 0
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "43e81a39",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:12.233842Z",
+     "start_time": "2025-03-20T14:07:11.520269Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from JSSEnv.envs import JssEnv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6224c9d2",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "source": [
+    "in this example we create the instance file instead of downloading it, because https://jobshop.jjvh.nl/ is\n",
+    "sometimes down. You can also download the instance from https://jobshop.jjvh.nl/ or use the download utils of\n",
+    "the package.\n",
+    "\n",
+    "Note: this repo also contains .txt files with instances from https://jobshop.jjvh.nl/ in the resources folder. So that\n",
+    "      is an alternative to downloading the instances from the website."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1102ac81",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:12.239251Z",
+     "start_time": "2025-03-20T14:07:12.234847Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# setup the jsp instance\n",
+    "instance_text = \"\"\"6\t6 \n",
+    "2\t1\t0\t3\t1\t6\t3\t7\t5\t3\t4\t6\n",
+    "1\t8\t2\t5\t4\t10\t5\t10\t0\t10\t3\t4\n",
+    "2\t5\t3\t4\t5\t8\t0\t9\t1\t1\t4\t7\n",
+    "1\t5\t0\t5\t2\t5\t3\t3\t4\t8\t5\t9\n",
+    "2\t9\t1\t3\t4\t5\t5\t4\t0\t3\t3\t1\n",
+    "1\t3\t3\t3\t5\t9\t0\t10\t4\t4\t2\t1\n",
+    "\"\"\"\n",
+    "jsp_std_path = \"ft06.txt\"\n",
+    "# write the instance to a file\n",
+    "with open(jsp_std_path, \"w\") as f:\n",
+    "    f.write(instance_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9973da59",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:12.249090Z",
+     "start_time": "2025-03-20T14:07:12.240455Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# create the environment\n",
+    "env = JssEnv(env_config={'instance_path': jsp_std_path})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f1378619",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:12.253750Z",
+     "start_time": "2025-03-20T14:07:12.250232Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# reset the environment\n",
+    "obs = env.reset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b66d90c9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:12.261272Z",
+     "start_time": "2025-03-20T14:07:12.254857Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# perform a random action till the environment is done\n",
+    "done = False\n",
+    "while not done:\n",
+    "    # sample a random action\n",
+    "    mask = env.unwrapped.legal_actions.astype(np.int8)\n",
+    "    action = env.action_space.sample(mask=mask)\n",
+    "    # take a step in the environment\n",
+    "    obs, reward, done, info = env.step(action)\n",
+    "\n",
+    "    # render the environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "078575db",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:12.308383Z",
+     "start_time": "2025-03-20T14:07:12.261349Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "makespan: 80\n"
+     ]
+    }
+   ],
+   "source": [
+    "env.render()\n",
+    "print(f\"makespan: {env.unwrapped.last_time_step}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3 (ipykernel)"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/examples/example-gallery.md b/docs/source/examples/example-gallery.md
new file mode 100644
index 0000000..88181cd
--- /dev/null
+++ b/docs/source/examples/example-gallery.md
@@ -0,0 +1,13 @@
+# Example Gallery
+
+This gallery contains a collection of code snippets together with their corresponding output, illustrating the usages of
+the JSP instances with various state-of-the-art algorithms.
+sfd
+
+```{toctree}
+:caption: 'Examples:'
+:maxdepth: 2
+
+
+JSSEnv_example
+```
diff --git a/docs/source/examples/graph-jsp-env_example.ipynb b/docs/source/examples/graph-jsp-env_example.ipynb
new file mode 100644
index 0000000..fbf7f85
--- /dev/null
+++ b/docs/source/examples/graph-jsp-env_example.ipynb
@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Graph JSP Environment Example"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "176ef8c2bf9c370d"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e5d7469c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:06:32.926914Z",
+     "start_time": "2025-03-20T14:06:32.921568Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jsp_instance_utils.instances import ft06, ft06_makespan\n",
+    "from graph_jsp_env.disjunctive_graph_jsp_env import DisjunctiveGraphJspEnv\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ccb42bf5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:06:32.938151Z",
+     "start_time": "2025-03-20T14:06:32.932725Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def main():\n",
+    "    env = DisjunctiveGraphJspEnv(\n",
+    "        jps_instance=ft06,\n",
+    "        reward_function_parameters={\n",
+    "            \"scaling_divisor\": ft06_makespan\n",
+    "        },\n",
+    "    )\n",
+    "\n",
+    "    done = False\n",
+    "    info = {}\n",
+    "    while not done:\n",
+    "        # get valid action mask. sample expects it to be a numpy array of type int8\n",
+    "        mask = np.array(env.valid_action_mask()).astype(np.int8)\n",
+    "        action = env.action_space.sample(mask=mask)\n",
+    "        state, reward, terminated, truncated, info = env.step(action)\n",
+    "\n",
+    "        done = terminated or truncated\n",
+    "\n",
+    "    # console rendering\n",
+    "    env.render(show=[\"gantt_console\", \"graph_console\"])\n",
+    "    print(f\"makespan: {info['makespan']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0351dedb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:06:32.967717Z",
+     "start_time": "2025-03-20T14:06:32.940230Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Job 0     \u001B[38;2;76;242;206mâ—\u001B[0m-\u001B[38;2;127;0;255mâ—\u001B[0m-\u001B[38;2;25;149;242mâ—\u001B[0m-\u001B[38;2;178;242;149mâ—\u001B[0m-\u001B[38;2;255;0;0mâ—\u001B[0m-\u001B[38;2;255;149;78mâ—\u001B[0m     Machine 0 \u001B[38;2;127;0;255mâ–ˆ\u001B[0m\n",
+      "Job 1     \u001B[38;2;25;149;242mâ—\u001B[0m-\u001B[38;2;76;242;206mâ—\u001B[0m-\u001B[38;2;255;149;78mâ—\u001B[0m-\u001B[38;2;255;0;0mâ—\u001B[0m-\u001B[38;2;127;0;255mâ—\u001B[0m-\u001B[38;2;178;242;149mâ—\u001B[0m     Machine 1 \u001B[38;2;25;149;242mâ–ˆ\u001B[0m\n",
+      "Job 2     \u001B[38;2;76;242;206mâ—\u001B[0m-\u001B[38;2;178;242;149mâ—\u001B[0m-\u001B[38;2;255;0;0mâ—\u001B[0m-\u001B[38;2;127;0;255mâ—\u001B[0m-\u001B[38;2;25;149;242mâ—\u001B[0m-\u001B[38;2;255;149;78mâ—\u001B[0m     Machine 2 \u001B[38;2;76;242;206mâ–ˆ\u001B[0m\n",
+      "Job 3     \u001B[38;2;25;149;242mâ—\u001B[0m-\u001B[38;2;127;0;255mâ—\u001B[0m-\u001B[38;2;76;242;206mâ—\u001B[0m-\u001B[38;2;178;242;149mâ—\u001B[0m-\u001B[38;2;255;149;78mâ—\u001B[0m-\u001B[38;2;255;0;0mâ—\u001B[0m     Machine 3 \u001B[38;2;178;242;149mâ–ˆ\u001B[0m\n",
+      "Job 4     \u001B[38;2;76;242;206mâ—\u001B[0m-\u001B[38;2;25;149;242mâ—\u001B[0m-\u001B[38;2;255;149;78mâ—\u001B[0m-\u001B[38;2;255;0;0mâ—\u001B[0m-\u001B[38;2;127;0;255mâ—\u001B[0m-\u001B[38;2;178;242;149mâ—\u001B[0m     Machine 4 \u001B[38;2;255;149;78mâ–ˆ\u001B[0m\n",
+      "Job 5     \u001B[38;2;25;149;242mâ—\u001B[0m-\u001B[38;2;178;242;149mâ—\u001B[0m-\u001B[38;2;255;0;0mâ—\u001B[0m-\u001B[38;2;127;0;255mâ—\u001B[0m-\u001B[38;2;255;149;78mâ—\u001B[0m-\u001B[38;2;76;242;206mâ—\u001B[0m     Machine 5 \u001B[38;2;255;0;0mâ–ˆ\u001B[0m\n",
+      "         â•”â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•—\n",
+      "Job 0    â•‘           \u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ \u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ            \u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ       \u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ   \u001B[0mâ•‘ Machine 0   \u001B[38;2;127;0;255mâ–ˆ\u001B[0m\n",
+      "Job 1    â•‘          \u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ  \u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ      \u001B[0mâ•‘ Machine 1   \u001B[38;2;25;149;242mâ–ˆ\u001B[0m\n",
+      "Job 2    â•‘        \u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ  \u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ                  \u001B[0mâ•‘ Machine 2   \u001B[38;2;76;242;206mâ–ˆ\u001B[0m\n",
+      "Job 3    â•‘\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ    \u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ                      \u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[0mâ•‘ Machine 3   \u001B[38;2;178;242;149mâ–ˆ\u001B[0m\n",
+      "Job 4    â•‘\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ                                   \u001B[0mâ•‘ Machine 4   \u001B[38;2;255;149;78mâ–ˆ\u001B[0m\n",
+      "Job 5    â•‘    \u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ              \u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ             \u001B[0mâ•‘ Machine 5   \u001B[38;2;255;0;0mâ–ˆ\u001B[0m\n",
+      "         â•šâ•¦â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¦â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¦â•â•â•â•â•\n",
+      "          0.0                      31.8                     63.6 \n",
+      "makespan: 69\n"
+     ]
+    }
+   ],
+   "source": [
+    "if __name__ == '__main__':\n",
+    "    main()"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3 (ipykernel)"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/examples/graph-matrix-jsp-env_example.ipynb b/docs/source/examples/graph-matrix-jsp-env_example.ipynb
new file mode 100644
index 0000000..4d1a04d
--- /dev/null
+++ b/docs/source/examples/graph-matrix-jsp-env_example.ipynb
@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Graph Matrix JSP Environment Example"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "1316e71f33ce6068"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "91390c73",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:06.572993Z",
+     "start_time": "2025-03-20T14:07:06.569181Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jsp_instance_utils.instances import ft06, ft06_makespan\n",
+    "from graph_matrix_jsp_env.disjunctive_jsp_env import DisjunctiveGraphJspEnv\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "56178ca2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:06.579293Z",
+     "start_time": "2025-03-20T14:07:06.574011Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def main():\n",
+    "    env = DisjunctiveGraphJspEnv(\n",
+    "        jsp_instance=ft06,\n",
+    "        c_lb=ft06_makespan,\n",
+    "        reward_function=\"makespan\"\n",
+    "    )\n",
+    "\n",
+    "    done = False\n",
+    "    info = {}\n",
+    "    while not done:\n",
+    "        # get valid action mask. sample expects it to be a numpy array of type int8\n",
+    "        mask = np.array(env.valid_action_mask()).astype(np.int8)\n",
+    "        action = env.action_space.sample(mask=mask)\n",
+    "        state, reward, terminated, truncated, info = env.step(action)\n",
+    "\n",
+    "        done = terminated or truncated\n",
+    "\n",
+    "    # console rendering\n",
+    "    env.render()\n",
+    "    print(f\"makespan: {env.get_makespan()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "d72df605",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:06.625781Z",
+     "start_time": "2025-03-20T14:07:06.580298Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         â•”â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•—\n",
+      "Job 0    â•‘    \u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ  \u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ           \u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ                   \u001B[0mâ•‘ Machine 0   \u001B[38;2;127;0;255mâ–ˆ\u001B[0m\n",
+      "Job 1    â•‘   \u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ   \u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ    \u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ     \u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ     \u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[0mâ•‘ Machine 1   \u001B[38;2;25;149;242mâ–ˆ\u001B[0m\n",
+      "Job 2    â•‘\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ                        \u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ \u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ   \u001B[0mâ•‘ Machine 2   \u001B[38;2;76;242;206mâ–ˆ\u001B[0m\n",
+      "Job 3    â•‘                \u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ     \u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ     \u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ  \u001B[0mâ•‘ Machine 3   \u001B[38;2;178;242;149mâ–ˆ\u001B[0m\n",
+      "Job 4    â•‘     \u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ \u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ                             \u001B[0mâ•‘ Machine 4   \u001B[38;2;255;149;78mâ–ˆ\u001B[0m\n",
+      "Job 5    â•‘\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ                \u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ  \u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ              \u001B[0mâ•‘ Machine 5   \u001B[38;2;255;0;0mâ–ˆ\u001B[0m\n",
+      "         â•šâ•¦â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¦â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¦â•â•â•â•â•\n",
+      "          0.0                      32.7                     65.5 \n",
+      "makespan: 71\n"
+     ]
+    }
+   ],
+   "source": [
+    "if __name__ == '__main__':\n",
+    "    main()"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3 (ipykernel)"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/examples/or-tools_example.ipynb b/docs/source/examples/or-tools_example.ipynb
new file mode 100644
index 0000000..b4e7ede
--- /dev/null
+++ b/docs/source/examples/or-tools_example.ipynb
@@ -0,0 +1,114 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# OR-Tools "
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "84274c1d3d525140"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7cd1f812",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:20.806162Z",
+     "start_time": "2025-03-20T14:07:20.428224Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jsp_instance_utils.instances import ft06, ft06_makespan\n",
+    "from jsp_instance_utils.jsp_or_tools_solver import solve_jsp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7fe0d3db",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:20.921691Z",
+     "start_time": "2025-03-20T14:07:20.807179Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         â•”â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•—\n",
+      "Job 0    â•‘     \u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ       \u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ        \u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ \u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ \u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ       \u001B[0mâ•‘ Machine 0   \u001B[38;2;127;0;255mâ–ˆ\u001B[0m\n",
+      "Job 1    â•‘\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ     \u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ   \u001B[0mâ•‘ Machine 1   \u001B[38;2;25;149;242mâ–ˆ\u001B[0m\n",
+      "Job 2    â•‘\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ \u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ                    \u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[0mâ•‘ Machine 2   \u001B[38;2;76;242;206mâ–ˆ\u001B[0m\n",
+      "Job 3    â•‘        \u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ    \u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ       \u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ \u001B[0mâ•‘ Machine 3   \u001B[38;2;178;242;149mâ–ˆ\u001B[0m\n",
+      "Job 4    â•‘             \u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ           \u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ   \u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ \u001B[38;2;178;242;149mâ–ˆ  \u001B[0mâ•‘ Machine 4   \u001B[38;2;255;149;78mâ–ˆ\u001B[0m\n",
+      "Job 5    â•‘             \u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;25;149;242mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;178;242;149mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;255;0;0mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;127;0;255mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;255;149;78mâ–ˆ\u001B[38;2;76;242;206mâ–ˆ            \u001B[0mâ•‘ Machine 5   \u001B[38;2;255;0;0mâ–ˆ\u001B[0m\n",
+      "         â•šâ•¦â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¦â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¤â•â•â•â•â•¦â•â•â•â•â•\n",
+      "          0.0                      25.5                     50.9 \n"
+     ]
+    }
+   ],
+   "source": [
+    "makespan, status, *_ = solve_jsp(jsp_instance=ft06, plot_results=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4256f487",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:20.926407Z",
+     "start_time": "2025-03-20T14:07:20.922918Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "assert status == \"OPTIMAL\"\n",
+    "assert makespan == ft06_makespan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7756ff00",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T14:07:20.931369Z",
+     "start_time": "2025-03-20T14:07:20.927447Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "makespan: 55.0 (OPTIMAL solution)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"makespan: {makespan} ({status} solution)\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3 (ipykernel)"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/index.md b/docs/source/index.md
new file mode 100644
index 0000000..f8884d5
--- /dev/null
+++ b/docs/source/index.md
@@ -0,0 +1,16 @@
+
+```{include} ../../README.md
+:relative-images:
+```
+
+
+```{toctree}
+:caption: 'Contents:'
+:maxdepth: 2
+
+
+instances-table
+source-code-docs/source-code
+examples/example-gallery
+related/related-projects
+```
diff --git a/docs/source/index.rst b/docs/source/index.rst
deleted file mode 100644
index 5738bd3..0000000
--- a/docs/source/index.rst
+++ /dev/null
@@ -1,17 +0,0 @@
-.. jsp-instance-utils documentation master file, created by
-   sphinx-quickstart on Wed Mar 19 16:40:55 2025.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
-jsp-instance-utils documentation
-================================
-
-Add your content using ``reStructuredText`` syntax. See the
-`reStructuredText <https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html>`_
-documentation for details.
-
-
-.. toctree::
-   :maxdepth: 2
-   :caption: Contents:
-
diff --git a/docs/source/instances-table.md b/docs/source/instances-table.md
new file mode 100644
index 0000000..66b0168
--- /dev/null
+++ b/docs/source/instances-table.md
@@ -0,0 +1,369 @@
+# Table of Instances
+
+This section is based on the GitHub Repo [jsspInstancesAndResults](https://github.com/thomasWeise/jsspInstancesAndResults) by Thomas Weise.
+The linked Repo is essentially a equivalent to this one, but for the programming language R.
+
+The rows have the following meaning:
+
+- `id` the unique identifier of the instance, as used in the literature (unsolved instances are marked in **bold**)
+- `ref` the reference to the publication where the instance was first mentioned/created
+- `jobs` the number of jobs in the instance
+- `machines` the number of machines in the instance
+- `lb` the lower bound for the makespan of any solution for the instance
+- `lb ref` the reference to the earliest publication (in this survey) that mentioned this lower bound
+- `bks` the makespan of the best-known solution (in terms of the makespan), based on this survey
+- `bks ref` the reference(s) to the earliest publication(s) in this survey that mentioned the bks
+- `t(bks) in s` the fastest time reported (in seconds), by any of the references in the study, for reaching `bks`
+- `t(bks) ref` the reference(s) of the publications reporting `t(bks)`
+
+Please, please take the column `t(bks)` with many grains of salt.
+First, we just report the time, regardless of which computer was used to obtain the result or even whether parallelism was applied or not.
+Second sometimes a minimum time to reach the best result of the run is given in a paper, sometimes we just have the maximum runtime used, sometimes we have a buget &ndash; and some publications do not report a runtime at all.
+Hence, our data here is very incomplete and unreliable and for some instances, we may not have any proper runtime value at all
+Therefore, this column is not to be understood as a normative a reliable information, more as a very rough guide regarding where we are standing right now.
+And, needless to say, it is only populated with the information extracted from the papers used in this study, so it may not even be representative.
+
+|id|ref|jobs|machines|lb|lb ref|bks|bks ref|t(bks) in s|t(bks) ref|
+|---:|:---:|---:|---:|---:|:---:|---:|:---:|---:|:---:|
+|abz5|<a href="#ABZ">ABZ</a>|10|10|1234|<a href="#AC">AC</a>|1234|<a href="#AC">AC</a>|0.04|<a href="#AZ">AZ</a>|
+|abz6|<a href="#ABZ">ABZ</a>|10|10|943|<a href="#AC">AC</a>|943|<a href="#AC">AC</a>|0.03|<a href="#AZ">AZ</a>|
+|abz7|<a href="#ABZ">ABZ</a>|20|15|656|<a href="#M">M</a>|656|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|**abz8**|<a href="#ABZ">ABZ</a>|20|15|**648**|<a href="#VLS">VLS</a>|**665**|<a href="#H">H</a>|**1000**|<a href="#H">H</a>|
+|abz9|<a href="#ABZ">ABZ</a>|20|15|678|<a href="#KNF">KNF</a>|678|<a href="#ZSR">ZSR</a>|3.25|<a href="#AZ">AZ</a>|
+|**dmu01**|<a href="#DMU1">DMU1</a>|20|15|**2501**|<a href="#BB">BB</a>|**2563**|<a href="#H">H</a>|**332.87**|<a href="#PLC">PLC</a>|
+|**dmu02**|<a href="#DMU1">DMU1</a>|20|15|**2651**|<a href="#BB">BB</a>|**2706**|<a href="#H">H</a>|**179.24**|<a href="#PLC">PLC</a>|
+|dmu03|<a href="#DMU1">DMU1</a>|20|15|2731|<a href="#BB">BB</a>|2731|<a href="#H">H</a>|388.59|<a href="#PLC">PLC</a>|
+|**dmu04**|<a href="#DMU1">DMU1</a>|20|15|**2601**|<a href="#BB">BB</a>|**2669**|<a href="#H">H</a>|**96.54**|<a href="#PLC">PLC</a>|
+|dmu05|<a href="#DMU1">DMU1</a>|20|15|2749|<a href="#BB">BB</a>|2749|<a href="#H">H</a>|303|<a href="#PLC">PLC</a>|
+|**dmu06**|<a href="#DMU1">DMU1</a>|20|20|**3042**|<a href="#vH2">vH2</a>|**3244**|<a href="#PSV">PSV</a>|**10000**|<a href="#PSV">PSV</a>|
+|**dmu07**|<a href="#DMU1">DMU1</a>|20|20|**2828**|<a href="#vH2">vH2</a>|**3046**|<a href="#PSV">PSV</a>|**360.58**|<a href="#PLC">PLC</a>|
+|**dmu08**|<a href="#DMU1">DMU1</a>|20|20|**3051**|<a href="#GL">GL</a>|**3188**|<a href="#PSV">PSV</a>|**295.81**|<a href="#PLC">PLC</a>|
+|**dmu09**|<a href="#DMU1">DMU1</a>|20|20|**2956**|<a href="#GL">GL</a>|**3092**|<a href="#H">H</a>|**500**|<a href="#H">H</a>|
+|**dmu10**|<a href="#DMU1">DMU1</a>|20|20|**2858**|<a href="#GL">GL</a>|**2984**|<a href="#PSV">PSV</a>|**10000**|<a href="#PSV">PSV</a>|
+|**dmu11**|<a href="#DMU1">DMU1</a>|30|15|**3395**|<a href="#DMU">DMU</a>|**3430**|<a href="#PLC">PLC</a>|**1496.85**|<a href="#PLC">PLC</a>|
+|**dmu12**|<a href="#DMU1">DMU1</a>|30|15|**3481**|<a href="#DMU">DMU</a>|**3492**|<a href="#SS">SS</a>|||
+|dmu13|<a href="#DMU1">DMU1</a>|30|15|3681|<a href="#DMU">DMU</a>|3681|<a href="#GR">GR</a>|622.13|<a href="#PLC">PLC</a>|
+|dmu14|<a href="#DMU1">DMU1</a>|30|15|3394|<a href="#DMU">DMU</a>|3394|<a href="#H">H</a>|3.02|<a href="#PLC">PLC</a>|
+|dmu15|<a href="#DMU1">DMU1</a>|30|15|3343|<a href="#GL">GL</a>|3343|<a href="#H">H</a>|1.77|<a href="#PLC">PLC</a>|
+|**dmu16**|<a href="#DMU1">DMU1</a>|30|20|**3734**|<a href="#GL">GL</a>|**3751**|<a href="#GR">GR</a>|||
+|**dmu17**|<a href="#DMU1">DMU1</a>|30|20|**3709**|<a href="#GL">GL</a>|**3814**|<a href="#SS">SS</a>|||
+|dmu18|<a href="#DMU1">DMU1</a>|30|20|3844|<a href="#DMU">DMU</a>|3844|<a href="#GR">GR</a>|3787.4|<a href="#PLC">PLC</a>|
+|**dmu19**|<a href="#DMU1">DMU1</a>|30|20|**3672**|<a href="#vH2">vH2</a>|**3765**|<a href="#SS">SS</a>|||
+|**dmu20**|<a href="#DMU1">DMU1</a>|30|20|**3604**|<a href="#DMU">DMU</a>|**3710**|<a href="#PLC">PLC</a>|**701.29**|<a href="#PLC">PLC</a>|
+|dmu21|<a href="#DMU1">DMU1</a>|40|15|4380|<a href="#DMU">DMU</a>|4380|<a href="#H">H</a>|0.69|<a href="#PLC">PLC</a>|
+|dmu22|<a href="#DMU1">DMU1</a>|40|15|4725|<a href="#DMU">DMU</a>|4725|<a href="#H">H</a>|1.48|<a href="#PLC">PLC</a>|
+|dmu23|<a href="#DMU1">DMU1</a>|40|15|4668|<a href="#DMU">DMU</a>|4668|<a href="#H">H</a>|1.3|<a href="#PLC">PLC</a>|
+|dmu24|<a href="#DMU1">DMU1</a>|40|15|4648|<a href="#DMU">DMU</a>|4648|<a href="#H">H</a>|0.75|<a href="#PLC">PLC</a>|
+|dmu25|<a href="#DMU1">DMU1</a>|40|15|4164|<a href="#DMU">DMU</a>|4164|<a href="#H">H</a>|0.6|<a href="#PLC">PLC</a>|
+|dmu26|<a href="#DMU1">DMU1</a>|40|20|4647|<a href="#DMU">DMU</a>|4647|<a href="#GR">GR</a>|1631.43|<a href="#PLC">PLC</a>|
+|dmu27|<a href="#DMU1">DMU1</a>|40|20|4848|<a href="#DMU">DMU</a>|4848|<a href="#H">H</a>|12.16|<a href="#PLC">PLC</a>|
+|dmu28|<a href="#DMU1">DMU1</a>|40|20|4692|<a href="#DMU">DMU</a>|4692|<a href="#H">H</a>|17.68|<a href="#PLC">PLC</a>|
+|dmu29|<a href="#DMU1">DMU1</a>|40|20|4691|<a href="#DMU">DMU</a>|4691|<a href="#H">H</a>|63.49|<a href="#PLC">PLC</a>|
+|dmu30|<a href="#DMU1">DMU1</a>|40|20|4732|<a href="#DMU">DMU</a>|4732|<a href="#H">H</a>|123|<a href="#PLC">PLC</a>|
+|dmu31|<a href="#DMU1">DMU1</a>|50|15|5640|<a href="#DMU">DMU</a>|5640|<a href="#H">H</a>|0.84|<a href="#PLC">PLC</a>|
+|dmu32|<a href="#DMU1">DMU1</a>|50|15|5927|<a href="#DMU">DMU</a>|5927|<a href="#H">H</a>|0.62|<a href="#PLC">PLC</a>|
+|dmu33|<a href="#DMU1">DMU1</a>|50|15|5728|<a href="#DMU">DMU</a>|5728|<a href="#H">H</a>|0.43|<a href="#PLC">PLC</a>|
+|dmu34|<a href="#DMU1">DMU1</a>|50|15|5385|<a href="#DMU">DMU</a>|5385|<a href="#H">H</a>|2.22|<a href="#PLC">PLC</a>|
+|dmu35|<a href="#DMU1">DMU1</a>|50|15|5635|<a href="#DMU">DMU</a>|5635|<a href="#H">H</a>|0.71|<a href="#PLC">PLC</a>|
+|dmu36|<a href="#DMU1">DMU1</a>|50|20|5621|<a href="#DMU">DMU</a>|5621|<a href="#H">H</a>|7.83|<a href="#PLC">PLC</a>|
+|dmu37|<a href="#DMU1">DMU1</a>|50|20|5851|<a href="#DMU">DMU</a>|5851|<a href="#H">H</a>|11.38|<a href="#PLC">PLC</a>|
+|dmu38|<a href="#DMU1">DMU1</a>|50|20|5713|<a href="#DMU">DMU</a>|5713|<a href="#H">H</a>|10.66|<a href="#PLC">PLC</a>|
+|dmu39|<a href="#DMU1">DMU1</a>|50|20|5747|<a href="#DMU">DMU</a>|5747|<a href="#H">H</a>|2.02|<a href="#PLC">PLC</a>|
+|dmu40|<a href="#DMU1">DMU1</a>|50|20|5577|<a href="#DMU">DMU</a>|5577|<a href="#H">H</a>|4.91|<a href="#PLC">PLC</a>|
+|**dmu41**|<a href="#DMU1">DMU1</a>|20|15|**3007**|<a href="#GL">GL</a>|**3248**|<a href="#PLC">PLC</a>|**417.84**|<a href="#PLC">PLC</a>|
+|**dmu42**|<a href="#DMU1">DMU1</a>|20|15|**3224**|<a href="#vH2">vH2</a>|**3390**|<a href="#PLC">PLC</a>|**448.95**|<a href="#PLC">PLC</a>|
+|**dmu43**|<a href="#DMU1">DMU1</a>|20|15|**3292**|<a href="#GL">GL</a>|**3441**|<a href="#GR">GR</a>|**399.33**|<a href="#PLC">PLC</a>|
+|**dmu44**|<a href="#DMU1">DMU1</a>|20|15|**3299**|<a href="#vH2">vH2</a>|**3475**|<a href="#SS">SS</a>|||
+|**dmu45**|<a href="#DMU1">DMU1</a>|20|15|**3039**|<a href="#vH2">vH2</a>|**3272**|<a href="#GR">GR</a>|||
+|**dmu46**|<a href="#DMU1">DMU1</a>|20|20|**3575**|<a href="#GL">GL</a>|**4035**|<a href="#GR">GR</a>|**984.86**|<a href="#PLC">PLC</a>|
+|**dmu47**|<a href="#DMU1">DMU1</a>|20|20|**3522**|<a href="#GL">GL</a>|**3939**|<a href="#GR">GR</a>|||
+|**dmu48**|<a href="#DMU1">DMU1</a>|20|20|**3447**|<a href="#GL">GL</a>|**3763**|<a href="#SS">SS</a>|||
+|**dmu49**|<a href="#DMU1">DMU1</a>|20|20|**3403**|<a href="#GL">GL</a>|**3710**|<a href="#PLC">PLC</a>|**633.84**|<a href="#PLC">PLC</a>|
+|**dmu50**|<a href="#DMU1">DMU1</a>|20|20|**3496**|<a href="#GL">GL</a>|**3729**|<a href="#PLC">PLC</a>|**609.62**|<a href="#PLC">PLC</a>|
+|**dmu51**|<a href="#DMU1">DMU1</a>|30|15|**3954**|<a href="#vH2">vH2</a>|**4156**|<a href="#SS">SS</a>|||
+|**dmu52**|<a href="#DMU1">DMU1</a>|30|15|**4094**|<a href="#vH2">vH2</a>|**4311**|<a href="#PLC">PLC</a>|**2232.6**|<a href="#PLC">PLC</a>|
+|**dmu53**|<a href="#DMU1">DMU1</a>|30|15|**4141**|<a href="#GL">GL</a>|**4390**|<a href="#SS">SS</a>|||
+|**dmu54**|<a href="#DMU1">DMU1</a>|30|15|**4202**|<a href="#GL">GL</a>|**4362**|<a href="#SS">SS</a>|||
+|**dmu55**|<a href="#DMU1">DMU1</a>|30|15|**4146**|<a href="#vH2">vH2</a>|**4270**|<a href="#SS">SS</a>|||
+|**dmu56**|<a href="#DMU1">DMU1</a>|30|20|**4554**|<a href="#GL">GL</a>|**4941**|<a href="#PLC">PLC</a>|**3825.44**|<a href="#PLC">PLC</a>|
+|**dmu57**|<a href="#DMU1">DMU1</a>|30|20|**4302**|<a href="#GL">GL</a>|**4663**|<a href="#PLC">PLC</a>|**3649.41**|<a href="#PLC">PLC</a>|
+|**dmu58**|<a href="#DMU1">DMU1</a>|30|20|**4319**|<a href="#GL">GL</a>|**4708**|<a href="#PLC">PLC</a>|**3639.68**|<a href="#PLC">PLC</a>|
+|**dmu59**|<a href="#DMU1">DMU1</a>|30|20|**4219**|<a href="#vH2">vH2</a>|**4619**|<a href="#SS">SS</a>|||
+|**dmu60**|<a href="#DMU1">DMU1</a>|30|20|**4319**|<a href="#GL">GL</a>|**4739**|<a href="#SS">SS</a>|||
+|**dmu61**|<a href="#DMU1">DMU1</a>|40|15|**4917**|<a href="#GL">GL</a>|**5172**|<a href="#SS">SS</a>|||
+|**dmu62**|<a href="#DMU1">DMU1</a>|40|15|**5041**|<a href="#vH2">vH2</a>|**5251**|<a href="#SS">SS</a>|||
+|**dmu63**|<a href="#DMU1">DMU1</a>|40|15|**5111**|<a href="#GL">GL</a>|**5323**|<a href="#SS">SS</a>|||
+|**dmu64**|<a href="#DMU1">DMU1</a>|40|15|**5130**|<a href="#DMU">DMU</a>|**5240**|<a href="#SS">SS</a>|||
+|**dmu65**|<a href="#DMU1">DMU1</a>|40|15|**5107**|<a href="#vH2">vH2</a>|**5190**|<a href="#SS">SS</a>|||
+|**dmu66**|<a href="#DMU1">DMU1</a>|40|20|**5397**|<a href="#vH2">vH2</a>|**5717**|<a href="#PLC">PLC</a>|**9543.86**|<a href="#PLC">PLC</a>|
+|**dmu67**|<a href="#DMU1">DMU1</a>|40|20|**5589**|<a href="#GL">GL</a>|**5779**|<a href="#SS">SS</a>|||
+|**dmu68**|<a href="#DMU1">DMU1</a>|40|20|**5426**|<a href="#GL">GL</a>|**5765**|<a href="#SS">SS</a>|||
+|**dmu69**|<a href="#DMU1">DMU1</a>|40|20|**5423**|<a href="#GL">GL</a>|**5709**|<a href="#PLC">PLC</a>|**8107.63**|<a href="#PLC">PLC</a>|
+|**dmu70**|<a href="#DMU1">DMU1</a>|40|20|**5501**|<a href="#GL">GL</a>|**5889**|<a href="#SS">SS</a>|||
+|**dmu71**|<a href="#DMU1">DMU1</a>|50|15|**6080**|<a href="#GL">GL</a>|**6223**|<a href="#PLC">PLC</a>|**9835.11**|<a href="#PLC">PLC</a>|
+|**dmu72**|<a href="#DMU1">DMU1</a>|50|15|**6395**|<a href="#GL">GL</a>|**6463**|<a href="#SS">SS</a>|||
+|**dmu73**|<a href="#DMU1">DMU1</a>|50|15|**6001**|<a href="#GL">GL</a>|**6153**|<a href="#SS">SS</a>|||
+|**dmu74**|<a href="#DMU1">DMU1</a>|50|15|**6123**|<a href="#GL">GL</a>|**6196**|<a href="#SS">SS</a>|||
+|**dmu75**|<a href="#DMU1">DMU1</a>|50|15|**6029**|<a href="#GL">GL</a>|**6189**|<a href="#SS">SS</a>|||
+|**dmu76**|<a href="#DMU1">DMU1</a>|50|20|**6342**|<a href="#GL">GL</a>|**6807**|<a href="#SS">SS</a>|||
+|**dmu77**|<a href="#DMU1">DMU1</a>|50|20|**6499**|<a href="#GL">GL</a>|**6792**|<a href="#SS">SS</a>|||
+|**dmu78**|<a href="#DMU1">DMU1</a>|50|20|**6586**|<a href="#GL">GL</a>|**6770**|<a href="#PLC">PLC</a>|**10346.61**|<a href="#PLC">PLC</a>|
+|**dmu79**|<a href="#DMU1">DMU1</a>|50|20|**6650**|<a href="#GL">GL</a>|**6952**|<a href="#SS">SS</a>|||
+|**dmu80**|<a href="#DMU1">DMU1</a>|50|20|**6459**|<a href="#GL">GL</a>|**6673**|<a href="#SS">SS</a>|||
+|ft06|<a href="#FT">FT</a>|6|6|55|<a href="#FTM">FTM</a>|55|<a href="#CP">CP</a>|0|<a href="#AZ">AZ</a>|
+|ft10|<a href="#FT">FT</a>|10|10|930|<a href="#CP">CP</a>|930|<a href="#CP">CP</a>|0.06|<a href="#AZ">AZ</a>|
+|ft20|<a href="#FT">FT</a>|20|5|1165|<a href="#MF">MF</a>|1165|<a href="#CP">CP</a>|0.18|<a href="#PLC">PLC</a>|
+|la01|<a href="#L">L</a>|10|5|666|<a href="#ABZ">ABZ</a>|666|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la02|<a href="#L">L</a>|10|5|655|<a href="#ABZ">ABZ</a>|655|<a href="#AC">AC</a>|0.015|<a href="#AZ">AZ</a>|
+|la03|<a href="#L">L</a>|10|5|597|<a href="#AC">AC</a>|597|<a href="#AC">AC</a>|0.016|<a href="#AZ">AZ</a>|
+|la04|<a href="#L">L</a>|10|5|590|<a href="#AC">AC</a>|590|<a href="#AC">AC</a>|0.015|<a href="#AZ">AZ</a>|
+|la05|<a href="#L">L</a>|10|5|593|<a href="#ABZ">ABZ</a>|593|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la06|<a href="#L">L</a>|15|5|926|<a href="#ABZ">ABZ</a>|926|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la07|<a href="#L">L</a>|15|5|890|<a href="#ABZ">ABZ</a>|890|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la08|<a href="#L">L</a>|15|5|863|<a href="#ABZ">ABZ</a>|863|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la09|<a href="#L">L</a>|15|5|951|<a href="#ABZ">ABZ</a>|951|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la10|<a href="#L">L</a>|15|5|958|<a href="#ABZ">ABZ</a>|958|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la11|<a href="#L">L</a>|20|5|1222|<a href="#ABZ">ABZ</a>|1222|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la12|<a href="#L">L</a>|20|5|1039|<a href="#ABZ">ABZ</a>|1039|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la13|<a href="#L">L</a>|20|5|1150|<a href="#ABZ">ABZ</a>|1150|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la14|<a href="#L">L</a>|20|5|1292|<a href="#ABZ">ABZ</a>|1292|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la15|<a href="#L">L</a>|20|5|1207|<a href="#ABZ">ABZ</a>|1207|<a href="#AC">AC</a>|0.016|<a href="#AZ">AZ</a>|
+|la16|<a href="#L">L</a>|10|10|945|<a href="#CP1">CP1</a>|945|<a href="#AC">AC</a>|0.06|<a href="#CCC">CCC</a>|
+|la17|<a href="#L">L</a>|10|10|784|<a href="#CP1">CP1</a>|784|<a href="#AC">AC</a>|0.016|<a href="#AZ">AZ</a>|
+|la18|<a href="#L">L</a>|10|10|848|<a href="#AC">AC</a>|848|<a href="#AC">AC</a>|0.015|<a href="#AZ">AZ</a>|
+|la19|<a href="#L">L</a>|10|10|842|<a href="#AC">AC</a>|842|<a href="#AC">AC</a>|0.025|<a href="#AZ">AZ</a>|
+|la20|<a href="#L">L</a>|10|10|902|<a href="#AC">AC</a>|902|<a href="#AC">AC</a>|0.031|<a href="#AZ">AZ</a>|
+|la21|<a href="#L">L</a>|15|10|1046|<a href="#VAL">VAL</a>|1046|<a href="#YN1">YN1</a>|7.33|<a href="#PLC">PLC</a>|
+|la22|<a href="#L">L</a>|15|10|927|<a href="#AC">AC</a>|927|<a href="#AC">AC</a>|0.109|<a href="#AZ">AZ</a>|
+|la23|<a href="#L">L</a>|15|10|1032|<a href="#ABZ">ABZ</a>|1032|<a href="#AC">AC</a>|0.047|<a href="#AZ">AZ</a>|
+|la24|<a href="#L">L</a>|15|10|935|<a href="#AC">AC</a>|935|<a href="#AC">AC</a>|0.2|<a href="#AZ">AZ</a>|
+|la25|<a href="#L">L</a>|15|10|977|<a href="#AC">AC</a>|977|<a href="#AC">AC</a>|0.33|<a href="#AZ">AZ</a>|
+|la26|<a href="#L">L</a>|20|10|1218|<a href="#ABZ">ABZ</a>|1218|<a href="#AC">AC</a>|0.078|<a href="#AZ">AZ</a>|
+|la27|<a href="#L">L</a>|20|10|1235|<a href="#ABZ">ABZ</a>|1235|<a href="#YN1">YN1</a>|0.95|<a href="#AZ">AZ</a>|
+|la28|<a href="#L">L</a>|20|10|1216|<a href="#ABZ">ABZ</a>|1216|<a href="#AC">AC</a>|0.109|<a href="#AZ">AZ</a>|
+|la29|<a href="#L">L</a>|20|10|1152|<a href="#M">M</a>|1152|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|la30|<a href="#L">L</a>|20|10|1355|<a href="#ABZ">ABZ</a>|1355|<a href="#AC">AC</a>|0.093|<a href="#AZ">AZ</a>|
+|la31|<a href="#L">L</a>|30|10|1784|<a href="#ABZ">ABZ</a>|1784|<a href="#AC">AC</a>|0|<a href="#AZ">AZ</a>|
+|la32|<a href="#L">L</a>|30|10|1850|<a href="#ABZ">ABZ</a>|1850|<a href="#AC">AC</a>|0.047|<a href="#AZ">AZ</a>|
+|la33|<a href="#L">L</a>|30|10|1719|<a href="#ABZ">ABZ</a>|1719|<a href="#AC">AC</a>|0.031|<a href="#AZ">AZ</a>|
+|la34|<a href="#L">L</a>|30|10|1721|<a href="#ABZ">ABZ</a>|1721|<a href="#AC">AC</a>|0.156|<a href="#AZ">AZ</a>|
+|la35|<a href="#L">L</a>|30|10|1888|<a href="#ABZ">ABZ</a>|1888|<a href="#AC">AC</a>|0.046|<a href="#AZ">AZ</a>|
+|la36|<a href="#L">L</a>|15|15|1268|<a href="#CP1">CP1</a>|1268|<a href="#AC">AC</a>|0.57|<a href="#AZ">AZ</a>|
+|la37|<a href="#L">L</a>|15|15|1397|<a href="#AC">AC</a>|1397|<a href="#AC">AC</a>|0.51|<a href="#AZ">AZ</a>|
+|la38|<a href="#L">L</a>|15|15|1196|<a href="#VAL">VAL</a>|1196|<a href="#NS">NS</a>|1.25|<a href="#AZ">AZ</a>|
+|la39|<a href="#L">L</a>|15|15|1233|<a href="#AC">AC</a>|1233|<a href="#AC">AC</a>|0.5|<a href="#AZ">AZ</a>|
+|la40|<a href="#L">L</a>|15|15|1222|<a href="#AC">AC</a>|1222|<a href="#AC">AC</a>|384.8|<a href="#PLC">PLC</a>|
+|orb01|<a href="#AC">AC</a>|10|10|1059|<a href="#AC">AC</a>|1059|<a href="#AC">AC</a>|0.06|<a href="#AZ">AZ</a>|
+|orb02|<a href="#AC">AC</a>|10|10|888|<a href="#AC">AC</a>|888|<a href="#AC">AC</a>|0.06|<a href="#AZ">AZ</a>|
+|orb03|<a href="#AC">AC</a>|10|10|1005|<a href="#AC">AC</a>|1005|<a href="#AC">AC</a>|0.15|<a href="#AZ">AZ</a>|
+|orb04|<a href="#AC">AC</a>|10|10|1005|<a href="#AC">AC</a>|1005|<a href="#AC">AC</a>|0.1|<a href="#CCC">CCC</a>|
+|orb05|<a href="#AC">AC</a>|10|10|887|<a href="#AC">AC</a>|887|<a href="#AC">AC</a>|0.76|<a href="#AZ">AZ</a>|
+|orb06|<a href="#AC">AC</a>|10|10|1010|<a href="#JM">JM</a>|1010|<a href="#BV1">BV1</a>|0.72|<a href="#AZ">AZ</a>|
+|orb07|<a href="#AC">AC</a>|10|10|397|<a href="#JM">JM</a>|397|<a href="#H">H</a>|0.02|<a href="#AZ">AZ</a>|
+|orb08|<a href="#AC">AC</a>|10|10|899|<a href="#JM">JM</a>|899|<a href="#BV1">BV1</a>|0.09|<a href="#AZ">AZ</a>|
+|orb09|<a href="#AC">AC</a>|10|10|934|<a href="#JM">JM</a>|934|<a href="#BV1">BV1</a>|0.09|<a href="#AZ">AZ</a>|
+|orb10|<a href="#AC">AC</a>|10|10|944|<a href="#JM">JM</a>|944|<a href="#BV1">BV1</a>|0.03|<a href="#AZ">AZ</a>|
+|swv01|<a href="#SWV">SWV</a>|20|10|1407|<a href="#M">M</a>|1407|<a href="#H">H</a>|575.76|<a href="#PLC">PLC</a>|
+|swv02|<a href="#SWV">SWV</a>|20|10|1475|<a href="#M">M</a>|1475|<a href="#H">H</a>|136.94|<a href="#AZ">AZ</a>|
+|swv03|<a href="#SWV">SWV</a>|20|10|1398|<a href="#BB">BB</a>|1398|<a href="#H">H</a>|613|<a href="#PLC">PLC</a>|
+|swv04|<a href="#SWV">SWV</a>|20|10|1464|<a href="#VLS">VLS</a>|1464|<a href="#VLS2">VLS2</a>|30000|<a href="#VLS2">VLS2</a>|
+|swv05|<a href="#SWV">SWV</a>|20|10|1424|<a href="#M">M</a>|1424|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|**swv06**|<a href="#SWV">SWV</a>|20|15|**1630**|<a href="#VLS">VLS</a>|**1671**|<a href="#PLC">PLC</a>, <a href="#VLS2">VLS2</a>|**385.73**|<a href="#PLC">PLC</a>|
+|**swv07**|<a href="#SWV">SWV</a>|20|15|**1513**|<a href="#VLS">VLS</a>|**1594**|<a href="#GR">GR</a>|||
+|**swv08**|<a href="#SWV">SWV</a>|20|15|**1671**|<a href="#VLS">VLS</a>|**1752**|<a href="#PLC">PLC</a>, <a href="#VLS2">VLS2</a>|**503**|<a href="#PLC">PLC</a>|
+|**swv09**|<a href="#SWV">SWV</a>|20|15|**1633**|<a href="#VLS">VLS</a>|**1655**|<a href="#PLC">PLC</a>, <a href="#VLS2">VLS2</a>|**521.91**|<a href="#PLC">PLC</a>|
+|**swv10**|<a href="#SWV">SWV</a>|20|15|**1663**|<a href="#VLS">VLS</a>|**1743**|<a href="#GR">GR</a>|**441.4**|<a href="#PLC">PLC</a>|
+|swv11|<a href="#SWV">SWV</a>|50|10|2983|<a href="#V1">V1</a>|2983|<a href="#NS2">NS2</a>|940.68|<a href="#PLC">PLC</a>|
+|**swv12**|<a href="#SWV">SWV</a>|50|10|**2972**|<a href="#V1">V1</a>|**2977**|<a href="#PLC">PLC</a>|**6097.35**|<a href="#PLC">PLC</a>|
+|swv13|<a href="#SWV">SWV</a>|50|10|3104|<a href="#V1">V1</a>|3104|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|swv14|<a href="#SWV">SWV</a>|50|10|2968|<a href="#BV">BV</a>|2968|<a href="#H">H</a>|422.81|<a href="#PLC">PLC</a>|
+|swv15|<a href="#SWV">SWV</a>|50|10|2885|<a href="#V1">V1</a>|2885|<a href="#PLC">PLC</a>|6000.57|<a href="#PLC">PLC</a>|
+|swv16|<a href="#SWV">SWV</a>|50|10|2924|<a href="#SWV">SWV</a>|2924|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|swv17|<a href="#SWV">SWV</a>|50|10|2794|<a href="#SWV">SWV</a>|2794|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|swv18|<a href="#SWV">SWV</a>|50|10|2852|<a href="#SWV">SWV</a>|2852|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|swv19|<a href="#SWV">SWV</a>|50|10|2843|<a href="#SWV">SWV</a>|2843|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|swv20|<a href="#SWV">SWV</a>|50|10|2823|<a href="#SWV">SWV</a>|2823|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|ta01|<a href="#T">T</a>|15|15|1231|<a href="#T">T</a>|1231|<a href="#H">H</a>|2.93|<a href="#PLC">PLC</a>|
+|ta02|<a href="#T">T</a>|15|15|1244|<a href="#V">V</a>|1244|<a href="#NS">NS</a>|38.09|<a href="#PLC">PLC</a>|
+|ta03|<a href="#T">T</a>|15|15|1218|<a href="#BB">BB</a>|1218|<a href="#H">H</a>|43.66|<a href="#PLC">PLC</a>|
+|ta04|<a href="#T">T</a>|15|15|1175|<a href="#BB">BB</a>|1175|<a href="#PM">PM</a>|38.72|<a href="#PLC">PLC</a>|
+|ta05|<a href="#T">T</a>|15|15|1224|<a href="#BB">BB</a>|1224|<a href="#H">H</a>|11.24|<a href="#PLC">PLC</a>|
+|ta06|<a href="#T">T</a>|15|15|1238|<a href="#BB">BB</a>|1238|<a href="#H">H</a>|178.06|<a href="#PLC">PLC</a>|
+|ta07|<a href="#T">T</a>|15|15|1227|<a href="#BB">BB</a>|1227|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|ta08|<a href="#T">T</a>|15|15|1217|<a href="#BB">BB</a>|1217|<a href="#H">H</a>|2.43|<a href="#PLC">PLC</a>|
+|ta09|<a href="#T">T</a>|15|15|1274|<a href="#BB">BB</a>|1274|<a href="#H">H</a>|18.66|<a href="#PLC">PLC</a>|
+|ta10|<a href="#T">T</a>|15|15|1241|<a href="#V">V</a>|1241|<a href="#H">H</a>|42.25|<a href="#PLC">PLC</a>|
+|ta11|<a href="#T">T</a>|20|15|1357|<a href="#VLS">VLS</a>|1357|<a href="#BFW">BFW</a>|186.19|<a href="#PLC">PLC</a>|
+|ta12|<a href="#T">T</a>|20|15|1367|<a href="#VLS">VLS</a>|1367|<a href="#H">H</a>|206.06|<a href="#PLC">PLC</a>|
+|ta13|<a href="#T">T</a>|20|15|1342|<a href="#VLS">VLS</a>|1342|<a href="#H">H</a>|161.37|<a href="#PLC">PLC</a>|
+|ta14|<a href="#T">T</a>|20|15|1345|<a href="#V">V</a>|1345|<a href="#NS">NS</a>|6|<a href="#SS">SS</a>|
+|ta15|<a href="#T">T</a>|20|15|1339|<a href="#VLS">VLS</a>|1339|<a href="#PSV">PSV</a>|173.45|<a href="#PLC">PLC</a>|
+|ta16|<a href="#T">T</a>|20|15|1360|<a href="#VLS">VLS</a>|1360|<a href="#H">H</a>|63.41|<a href="#PLC">PLC</a>|
+|ta17|<a href="#T">T</a>|20|15|1462|<a href="#S">S</a>|1462|<a href="#H">H</a>|1000|<a href="#H">H</a>|
+|**ta18**|<a href="#T">T</a>|20|15|**1377**|<a href="#VLS">VLS</a>|**1396**|<a href="#H">H</a>|**91.13**|<a href="#PLC">PLC</a>|
+|ta19|<a href="#T">T</a>|20|15|1332|<a href="#VLS">VLS</a>|1332|<a href="#PSV">PSV</a>|145.42|<a href="#PLC">PLC</a>|
+|ta20|<a href="#T">T</a>|20|15|1348|<a href="#VLS">VLS</a>|1348|<a href="#PSV">PSV</a>|216.72|<a href="#PLC">PLC</a>|
+|ta21|<a href="#T">T</a>|20|20|1642|<a href="#VLS">VLS</a>|1642|<a href="#BFW">BFW</a>|3600|<a href="#BFW">BFW</a>|
+|**ta22**|<a href="#T">T</a>|20|20|**1561**|<a href="#VLS">VLS</a>|**1600**|<a href="#H">H</a>|**228.9**|<a href="#PLC">PLC</a>|
+|**ta23**|<a href="#T">T</a>|20|20|**1518**|<a href="#VLS">VLS</a>|**1557**|<a href="#H">H</a>|**359.79**|<a href="#PLC">PLC</a>|
+|ta24|<a href="#T">T</a>|20|20|1644|<a href="#VLS">VLS</a>|1644|<a href="#VLS2">VLS2</a>|30000|<a href="#VLS2">VLS2</a>|
+|**ta25**|<a href="#T">T</a>|20|20|**1558**|<a href="#VLS">VLS</a>|**1595**|<a href="#NS2">NS2</a>|**416.08**|<a href="#PLC">PLC</a>|
+|**ta26**|<a href="#T">T</a>|20|20|**1591**|<a href="#VLS">VLS</a>|**1643**|<a href="#GR">GR</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta27**|<a href="#T">T</a>|20|20|**1652**|<a href="#VLS">VLS</a>|**1680**|<a href="#H">H</a>|**254.74**|<a href="#PLC">PLC</a>|
+|ta28|<a href="#T">T</a>|20|20|1603|<a href="#VLS">VLS</a>|1603|<a href="#PSV">PSV</a>|1514|<a href="#SS">SS</a>|
+|**ta29**|<a href="#T">T</a>|20|20|**1573**|<a href="#VLS">VLS</a>|**1625**|<a href="#H">H</a>|**93.53**|<a href="#PLC">PLC</a>|
+|**ta30**|<a href="#T">T</a>|20|20|**1519**|<a href="#VLS">VLS</a>|**1584**|<a href="#H">H</a>|**388.66**|<a href="#PLC">PLC</a>|
+|ta31|<a href="#T">T</a>|30|15|1764|<a href="#T">T</a>|1764|<a href="#H">H</a>|6|<a href="#SS">SS</a>|
+|**ta32**|<a href="#T">T</a>|30|15|**1774**|<a href="#T">T</a>|**1784**|<a href="#S2">S2</a>|||
+|**ta33**|<a href="#T">T</a>|30|15|**1788**|<a href="#VLS">VLS</a>|**1791**|<a href="#PSV">PSV</a>|**457.55**|<a href="#PLC">PLC</a>|
+|**ta34**|<a href="#T">T</a>|30|15|**1828**|<a href="#T">T</a>|**1829**|<a href="#H">H</a>|**315.71**|<a href="#PLC">PLC</a>|
+|ta35|<a href="#T">T</a>|30|15|2007|<a href="#V">V</a>|2007|<a href="#PM">PM</a>|0.56|<a href="#PLC">PLC</a>|
+|ta36|<a href="#T">T</a>|30|15|1819|<a href="#V">V</a>|1819|<a href="#H">H</a>|15|<a href="#SS">SS</a>|
+|ta37|<a href="#T">T</a>|30|15|1771|<a href="#T">T</a>|1771|<a href="#GR">GR</a>|652.24|<a href="#PLC">PLC</a>|
+|ta38|<a href="#T">T</a>|30|15|1673|<a href="#T">T</a>|1673|<a href="#H">H</a>|45|<a href="#SS">SS</a>|
+|ta39|<a href="#T">T</a>|30|15|1795|<a href="#V">V</a>|1795|<a href="#H">H</a>|6|<a href="#SS">SS</a>|
+|**ta40**|<a href="#T">T</a>|30|15|**1651**|<a href="#VLS">VLS</a>|**1669**|<a href="#GR">GR</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta41**|<a href="#T">T</a>|30|20|**1906**|<a href="#VLS">VLS</a>|**2005**|<a href="#VLS2">VLS2</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta42**|<a href="#T">T</a>|30|20|**1884**|<a href="#VLS">VLS</a>|**1937**|<a href="#GR">GR</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta43**|<a href="#T">T</a>|30|20|**1809**|<a href="#V">V</a>|**1846**|<a href="#PLC">PLC</a>|**1726.78**|<a href="#PLC">PLC</a>|
+|**ta44**|<a href="#T">T</a>|30|20|**1948**|<a href="#VLS">VLS</a>|**1979**|<a href="#VLS2">VLS2</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta45**|<a href="#T">T</a>|30|20|**1997**|<a href="#V">V</a>|**2000**|<a href="#H">H</a>|**1057.79**|<a href="#PLC">PLC</a>|
+|**ta46**|<a href="#T">T</a>|30|20|**1957**|<a href="#VLS">VLS</a>|**2004**|<a href="#GR">GR</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta47**|<a href="#T">T</a>|30|20|**1807**|<a href="#VLS">VLS</a>|**1889**|<a href="#PLC">PLC</a>, <a href="#VLS2">VLS2</a>|**1030.88**|<a href="#PLC">PLC</a>|
+|**ta48**|<a href="#T">T</a>|30|20|**1912**|<a href="#V">V</a>|**1937**|<a href="#SS">SS</a>|**3008**|<a href="#SS">SS</a>|
+|**ta49**|<a href="#T">T</a>|30|20|**1931**|<a href="#VLS">VLS</a>|**1961**|<a href="#VLS2">VLS2</a>|**30000**|<a href="#VLS2">VLS2</a>|
+|**ta50**|<a href="#T">T</a>|30|20|**1833**|<a href="#VLS">VLS</a>|**1923**|<a href="#PLC">PLC</a>, <a href="#VLS2">VLS2</a>|**1318.05**|<a href="#PLC">PLC</a>|
+|ta51|<a href="#T">T</a>|50|15|2760|<a href="#T">T</a>|2760|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta52|<a href="#T">T</a>|50|15|2756|<a href="#T">T</a>|2756|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta53|<a href="#T">T</a>|50|15|2717|<a href="#T">T</a>|2717|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta54|<a href="#T">T</a>|50|15|2839|<a href="#T">T</a>|2839|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta55|<a href="#T">T</a>|50|15|2679|<a href="#T">T</a>|2679|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta56|<a href="#T">T</a>|50|15|2781|<a href="#T">T</a>|2781|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta57|<a href="#T">T</a>|50|15|2943|<a href="#T">T</a>|2943|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta58|<a href="#T">T</a>|50|15|2885|<a href="#T">T</a>|2885|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta59|<a href="#T">T</a>|50|15|2655|<a href="#T">T</a>|2655|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta60|<a href="#T">T</a>|50|15|2723|<a href="#T">T</a>|2723|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta61|<a href="#T">T</a>|50|20|2868|<a href="#T">T</a>|2868|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta62|<a href="#T">T</a>|50|20|2869|<a href="#V">V</a>|2869|<a href="#C">C</a>|||
+|ta63|<a href="#T">T</a>|50|20|2755|<a href="#T">T</a>|2755|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta64|<a href="#T">T</a>|50|20|2702|<a href="#BV">BV</a>|2702|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta65|<a href="#T">T</a>|50|20|2725|<a href="#T">T</a>|2725|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta66|<a href="#T">T</a>|50|20|2845|<a href="#T">T</a>|2845|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta67|<a href="#T">T</a>|50|20|2825|<a href="#V">V</a>|2825|<a href="#H">H</a>|2000|<a href="#H">H</a>|
+|ta68|<a href="#T">T</a>|50|20|2784|<a href="#BV">BV</a>|2784|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta69|<a href="#T">T</a>|50|20|3071|<a href="#T">T</a>|3071|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta70|<a href="#T">T</a>|50|20|2995|<a href="#T">T</a>|2995|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|ta71|<a href="#T">T</a>|100|20|5464|<a href="#T">T</a>|5464|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta72|<a href="#T">T</a>|100|20|5181|<a href="#T">T</a>|5181|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta73|<a href="#T">T</a>|100|20|5568|<a href="#T">T</a>|5568|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta74|<a href="#T">T</a>|100|20|5339|<a href="#T">T</a>|5339|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta75|<a href="#T">T</a>|100|20|5392|<a href="#T">T</a>|5392|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta76|<a href="#T">T</a>|100|20|5342|<a href="#T">T</a>|5342|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta77|<a href="#T">T</a>|100|20|5436|<a href="#T">T</a>|5436|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta78|<a href="#T">T</a>|100|20|5394|<a href="#T">T</a>|5394|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta79|<a href="#T">T</a>|100|20|5358|<a href="#T">T</a>|5358|<a href="#PM">PM</a>|2000|<a href="#H">H</a>|
+|ta80|<a href="#T">T</a>|100|20|5183|<a href="#T">T</a>|5183|<a href="#NS">NS</a>|2000|<a href="#H">H</a>|
+|yn1|<a href="#YN">YN</a>|20|20|884|<a href="#KNF">KNF</a>|884|<a href="#ZSR">ZSR</a>|169.29|<a href="#PLC">PLC</a>|
+|**yn2**|<a href="#YN">YN</a>|20|20|**870**|<a href="#BB">BB</a>|**904**|<a href="#GR">GR</a>|**202.22**|<a href="#PLC">PLC</a>|
+|**yn3**|<a href="#YN">YN</a>|20|20|**859**|<a href="#VLS">VLS</a>|**892**|<a href="#NS2">NS2</a>|**344.15**|<a href="#PLC">PLC</a>|
+|**yn4**|<a href="#YN">YN</a>|20|20|**929**|<a href="#VLS">VLS</a>|**968**|<a href="#H">H</a>|**320.51**|<a href="#PLC">PLC</a>|
+
+
+## Literature Sources
+
+<dl>
+<dt id="A">A</dt><dd>Abdelmaguid TF (2010). â€œRepresentations in Genetic Algorithm for the Job Shop Scheduling Problem: A Computational Study.â€ Journal of Software Engineering and Applications (JSEA), 3(12), 1155-1162. doi:<a href="https://doi.org/10.4236/jsea.2010.312135">10.4236/jsea.2010.312135</a>, <a href="http://www.scirp.org/journal/paperinformation.aspx?paperid=3561">http://www.scirp.org/journal/paperinformation.aspx?paperid=3561</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC58">A2010RIGAFTJSPACS</a></dd>
+<dt id="A2">A2</dt><dd>Asadzadeh L (2015). â€œA Local Search Genetic Algorithm for the Job Shop Scheduling Problem with Intelligent Agents.â€ Computers & Industrial Engineering, 85, 376-383. doi:<a href="https://doi.org/10.1016/j.cie.2015.04.006">10.1016/j.cie.2015.04.006</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC72">A2015ALSGAFTJSSPWIA</a></li>
+<dt id="ABZ">ABZ</dt><dd>Adams J, Balas E, Zawack D (1988). â€œThe Shifting Bottleneck Procedure for Job Shop Scheduling.â€ Management Science, 34(3), 391-401. doi:<a href="https://doi.org/10.1287/mnsc.34.3.391">10.1287/mnsc.34.3.391</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC84">ABZ1988TSBPFJSS</a></li>
+<dt id="AC">AC</dt><dd>Applegate DL, Cook WJ (1991). â€œA Computational Study of the Job-Shop Scheduling Problem.â€ ORSA Journal on Computing, 3(2), 149-156. doi:<a href="https://doi.org/10.1287/ijoc.3.2.149">10.1287/ijoc.3.2.149</a>, the JSSP instances used were generated in Bonn in 1986. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC96">AC1991ACSOTJSSP</a></li>
+<dt id="AF">AF</dt><dd>Aydin ME, Fogarty TC (2002). â€œModular Simulated Annealing for Job Shop Scheduling running on Distributed Resource Machine (DRM).â€ London South Bank University, Faculty of Business, Computing and Information Management, London, England, UK. <a href="http://www.soc.napier.ac.uk/~benp/dream/dreampaper6a.pdf">http://www.soc.napier.ac.uk/~benp/dream/dreampaper6a.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC109">AF2002MSAFJSSRODRMD</a></li>
+<dt id="AK">AK</dt><dd>Abdel-Kader RF (2018). â€œAn Improved PSO Algorithm with Genetic and Neighborhood-Based Diversity Operators for the Job Shop Scheduling Problem.â€ Applied Artificial Intelligence - An International Journal, 32(5), 433-462. doi:<a href="https://doi.org/10.1080/08839514.2018.1481903">10.1080/08839514.2018.1481903</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC119">AK2018AIPAWGANBDOFTJSSP</a></li>
+<dt id="AKZ">AKZ</dt><dd>Akram K, Kamal K, Zeb A (2016). â€œFast Simulated Annealing Hybridized with Quenching for Solving Job Shop Scheduling Problem.â€ Applied Soft Computing Journal (ASOC), 49, 510-523. doi:<a href="https://doi.org/10.1016/j.asoc.2016.08.037">10.1016/j.asoc.2016.08.037</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC132">AKZ2016FSAHWQFSJSSP</a></li>
+<dt id="AMC">AMC</dt><dd>Angel JM, MartÃnez MR, Castillo LRM, Solis LS (2014). â€œUn Modelo HÃbrido de Inteligencia Computacional para Resolver el Problema de Job Shop Scheduling.â€ Research in Computing Science, 79(Advances in Intelligent Information Technologies), 9-20. <a href="http://www.rcs.cic.ipn.mx/2014_79/RCS_79_2014.pdf">http://www.rcs.cic.ipn.mx/2014_79/RCS_79_2014.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC144">AMCS2014UMHDICPREPDJSS</a></li>
+<dt id="ASS">ASS</dt><dd>Amaria K, Souier M, Sar Z (2014). â€œArtificial Bee Colony (ABC) Algorithm for the Job-Shop Scheduling Problem.â€ In Proceedings of the 5th International Conference on Metaheuristics and Nature Inspired Computing (META'14), October 27-31, 2014, Marrakech, Morocco. The paper reports makespan 53 for ft06, which is below the lower bound of 55 and thus is not included in our dataset., <a href="https://meta2014.sciencesconf.org/42589/document">https://meta2014.sciencesconf.org/42589/document</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC157">ASS2014ABCAAFTJSSP</a></li>
+<dt id="AZ">AZ</dt><dd>Amirghasemi M, Zamani R (2015). â€œAn Effective Asexual Genetic Algorithm for Solving the Job Shop Scheduling Problem.â€ Computers & Industrial Engineering, 83, 123-138. doi:<a href="https://doi.org/10.1016/j.cie.2015.02.011">10.1016/j.cie.2015.02.011</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC166">AZ2015AEAGAFSTJSSP</a></li>
+<dt id="B">B</dt><dd>Bierwirth C (1995). â€œA Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms.â€ Operations-Research-Spektrum (OR Spectrum), 17(2-3), 87-92. doi:<a href="https://doi.org/10.1007/BF01719250">10.1007/BF01719250</a>, <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.52.7392&type=pdf">http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.52.7392&type=pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC178">B1995AGPATJSSWGA</a></li>
+<dt id="BB">BB</dt><dd>BrinkkÃ¶tter W, Brucker P (2001). â€œSolving Open Benchmark Instances for the Job-Shop Problem by Parallel Head-Tail Adjustments.â€ Journal of Scheduling, 4(1), 53-64. doi:<a href="https://doi.org/10.1002/1099-1425(200101/02">10.1002/1099-1425(200101/02)4:1<53::AID-JOS59>3.0.CO;2-Y</a>4:1<53::AID-JOS59>3.0.CO;2-Y). BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC192">BB2001SOBIFTJSPBPHTA</a></li>
+<dt id="BFW">BFW</dt><dd>Beck JC, Feng TK, Watson J (2011). â€œCombining Constraint Programming and Local Search for Job-Shop Scheduling.â€ INFORMS Journal on Computing, 23(1), 1-14. doi:<a href="https://doi.org/10.1287/ijoc.1100.0388">10.1287/ijoc.1100.0388</a>, <a href="http://cfwebprod.sandia.gov/cfdocs/CompResearch/docs/ists-sgmpcs.pdf">http://cfwebprod.sandia.gov/cfdocs/CompResearch/docs/ists-sgmpcs.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC204">BFW2011CCPALSFJSS</a></li>
+<dt id="BV">BV</dt><dd>Balas E, Vazacopoulos A (1994). â€œGuided Local Search with Shifting Bottleneck for Job Shop Scheduling.â€ Management Science Research Report MSSRâ€“609, Graduate School of Industrial Administration (GSIA), Carnegie Mellon University, Pittsburgh, PA, USA. revised November 1995. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC217">BV1994GLSWSBFJSS</a></li>
+<dt id="BV1">BV1</dt><dd>Balas E, Vazacopoulos A (1998). â€œGuided Local Search with Shifting Bottleneck for Job Shop Scheduling.â€ Management Science, 44(2), 262-275. doi:<a href="https://doi.org/10.1287/mnsc.44.2.262">10.1287/mnsc.44.2.262</a>, reports 307 as makespan for orb07, probably a typo, as the lower bound is 397. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC230">BV1998GLSWSBFJSS</a></li>
+<dt id="C">C</dt><dd>Caldeira JP (2003). â€œPrivate Communication of Result 2869 for ta62 to Ã‰ric D. Taillard, listed on Ã‰ric Taillard's Page.â€ <a href="http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/jobshop.dir/best_lb_up.txt">http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/jobshop.dir/best_lb_up.txt</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC244">C2003PCOR2FTTETLOETP</a></li>
+<dt id="CCC">CCC</dt><dd>Cruz-ChÃ¡vez MA, Cruz Rosales MH, Zavala-DÃaz JC, Aguilar JAH, RodrÄ±guez-LeÃ³ A, Avelino JCP, Orziz MEL, Salinas OH (2019). â€œHybrid Micro Genetic Multi-Population Algorithm With Collective Communication for the Job Shop Scheduling Problem.â€ IEEE Access, 7, 82358-82376. doi:<a href="https://doi.org/10.1109/ACCESS.2019.2924218">10.1109/ACCESS.2019.2924218</a>, <a href="http://ieeexplore.ieee.org/document/8743353">http://ieeexplore.ieee.org/document/8743353</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC253">CCCRZDARLAOS2019HMGMPAWCCFTJSSP</a></li>
+<dt id="CP">CP</dt><dd>Carlier J, Pinson Ã‰ (1989). â€œAn Algorithm for Solving the Job-Shop Problem.â€ Management Science, 35(2), 164-176. doi:<a href="https://doi.org/10.1287/mnsc.35.2.164">10.1287/mnsc.35.2.164</a>, jstor: 2631909. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC266">CP1989AAFSTJSP</a></li>
+<dt id="CP1">CP1</dt><dd>Carlier J, Pinson Ã‰ (1990). â€œA Practical Use of Jackson's Preemptive Schedule for Solving the Job Shop Problem.â€ Annals of Operations Research, 26(1-4), 269-287. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC279">CP1990APUOJPSFSTJSP</a></li>
+<dt id="CPL">CPL</dt><dd>Cheng TCE, Peng B, LÃ¼ Z (2016). â€œA Hybrid Evolutionary Algorithm to Solve the Job Shop Scheduling Problem.â€ Annals of Operations Research, 242(2), 223-237. doi:<a href="https://doi.org/10.1007/s10479-013-1332-5">10.1007/s10479-013-1332-5</a>, The paper reports 555 as average makespan of HEA for ft20, which is an obvious typo because the other columns have 1165, which is the lower bound. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC291">CPL2016AHEATSTJSSP</a></li>
+<dt id="DMU">DMU</dt><dd>Demirkol E, Mehta SV, Uzsoy R (1996). â€œBenchmarking for Shop Scheduling Problems.â€ Research Memorandum 96-4, School of Industrial Engineering, Purdue University, West Lafayette, IN, USA. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC305">DMU1996BFSSP</a></li>
+<dt id="DMU1">DMU1</dt><dd>Demirkol E, Mehta SV, Uzsoy R (1998). â€œBenchmarks for Shop Scheduling Problems.â€ European Journal of Operational Research (EJOR), 109(1), 137-141. doi:<a href="https://doi.org/10.1016/S0377-2217(97">10.1016/S0377-2217(97)00019-2</a>00019-2). BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC316">DMU1998BFSSP</a></li>
+<dt id="DPN">DPN</dt><dd>Dao T, Pan T, Nguyen T, Pan J (2018). â€œParallel Bat Algorithm for Optimizing Makespan in Job Shop Scheduling Problems.â€ Journal of Intelligent Manufacturing, 29(2), 451-462. doi:<a href="https://doi.org/10.1007/s10845-015-1121-x">10.1007/s10845-015-1121-x</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC329">DPNP2018PBAFOMIJSSP</a></li>
+<dt id="FGB">FGB</dt><dd>FlÃ³rez E, GÃ³mez W, Bautista L (2013). â€œAn Ant Colony Optimization Algorithm for Job Shop Scheduling Problem.â€ Computing Research Repository (CoRR) abs/1309.5110, arXiv. <a href="https://arxiv.org/pdf/1309.5110.pdf">https://arxiv.org/pdf/1309.5110.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC355">FGB2013AACOAFJSSP</a></li>
+<dt id="FT">FT</dt><dd>Fisher H, Thompson GL (1963). â€œProbabilistic Learning Combinations of Local Job-Shop Scheduling Rules.â€ In Muth JF, Thompson GL (eds.), Industrial Scheduling, 225-251. Prentice-Hall, Englewood Cliffs, NJ, USA. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC366">FT1963PLCOLJSSR</a></li>
+<dt id="FTM">FTM</dt><dd>Florian M, Trepant P, McMahon G (1971). â€œAn Implicit Enumeration Algorithm for the Machine Sequencing Problem.â€ Management Science, 17(12), B-782-B-792. doi:<a href="https://doi.org/10.1287/mnsc.17.12.B782">10.1287/mnsc.17.12.B782</a>, jstor: 2629469. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC379">FTM1971AIEAFTMSP</a></li>
+<dt id="GL">GL</dt><dd>Gharbi A, Labidi M (2010). â€œExtending the Single Machine-Based Relaxation Scheme for the Job Shop Scheduling Problem.â€ Electronic Notes in Discrete Mathematics, 36, 1057-1064. doi:<a href="https://doi.org/10.1016/j.endm.2010.05.134">10.1016/j.endm.2010.05.134</a>, this algorithm was used to solve several JSSP instances of the OR Library. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC393">GL2010ETSMBRSFTJSSP</a></li>
+<dt id="GLW">GLW</dt><dd>Gao L, Li X, Wen X, Lu C, Wen F (2015). â€œA Hybrid Algorithm based on a New Neighborhood Structure Evaluation Method for Job Shop Scheduling Problem.â€ Computers & Industrial Engineering, 88, 417-429. doi:<a href="https://doi.org/10.1016/j.cie.2015.08.002">10.1016/j.cie.2015.08.002</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC406">GLWLW2015AHABOANNSEMFSSP</a></li>
+<dt id="GR">GR</dt><dd>GonÃ§alves JF, Resende MGC (2014). â€œAn Extended Akers Graphical Method with a Biased Random-Key Genetic Algorithm for Job-Shop Scheduling.â€ International Transactions on Operational Research (ITOR), 21(2), 215-246. doi:<a href="https://doi.org/10.1111/itor.12044">10.1111/itor.12044</a>, <a href="http://mauricio.resende.info/doc/brkga-jss2011.pdf">http://mauricio.resende.info/doc/brkga-jss2011.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC418">GR2014AEAGMWABRKGAFJSS</a></li>
+<dt id="GTK">GTK</dt><dd>Gen M, Tsujimura Y, Kubota E (1994). â€œSolving Job-Shop Scheduling Problems by Genetic Algorithm.â€ In Humans, Information and Technology: Proceedings of the 1994 IEEE International Conference on Systems, Man and Cybernetics, October 2-5, 1994, San Antonio, TX, USA, volume 2. ISBN 0-7803-2129-4, doi:<a href="https://doi.org/10.1109/ICSMC.1994.400072">10.1109/ICSMC.1994.400072</a>, <a href="http://read.pudn.com/downloads151/doc/658565/00400072.pdf">http://read.pudn.com/downloads151/doc/658565/00400072.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC342">GTK1994SJSSPBGA</a></li>
+<dt id="GvH">GvH</dt><dd>Gromicho JAS, van Hoorn JJ, Saldanha-da-Gama F, Timmer GT (2009). â€œExponentially Better than Brute Force: Solving the Job-Shop Scheduling Problem Optimally by Dynamic Programming.â€ Research Memorandum 2009-56, Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. <a href="http://degree.ubvu.vu.nl/repec/vua/wpaper/pdf/20090056.pdf">http://degree.ubvu.vu.nl/repec/vua/wpaper/pdf/20090056.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC432">GvHSGT2009</a></li>
+<dt id="H">H</dt><dd>Henning A (2002). Praktische Job-Shop Scheduling-Probleme. Ph.D. thesis, Friedrich-Schiller-UniversitÃ¤t Jena, Jena, Germany. alternate url: https://nbn-resolving.org/urn:nbn:de:gbv:27-20060809-115700-4, <a href="http://www.db-thueringen.de/servlets/DocumentServlet?id=873">http://www.db-thueringen.de/servlets/DocumentServlet?id=873</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC444">H2002PJSSP</a></li>
+<dt id="HRS">HRS</dt><dd>HernÃ¡ndez-RamÃrez L, Solis JF, Castilla-Valdez G, GonzÃ¡lez-Barbosa JJ, TerÃ¡n-Villanueva D, Morales-RodrÃguez ML (2019). â€œA Hybrid Simulated Annealing for Job Shop Scheduling Problem.â€ International Journal of Combinatorial Optimization Problems and Informatics (IJCOPI), 10(1), 6-15. published 2018-08-10, <a href="http://ijcopi.org/index.php/ojs/article/view/111">http://ijcopi.org/index.php/ojs/article/view/111</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC457">HRSCVGBTVMR2019AHSAFJSSP</a></li>
+<dt id="HY">HY</dt><dd>Han B, Yang J (2020). â€œResearch on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN.â€ IEEE Access, 8, 186474-186495. doi:<a href="https://doi.org/10.1109/ACCESS.2020.3029868">10.1109/ACCESS.2020.3029868</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC471">HY2020ROAJSSPBODDD</a></li>
+<dt id="JM">JM</dt><dd>Jain AS, Meeran S (1999). â€œDeterministic Job-Shop Scheduling: Past, Present and Future.â€ European Journal of Operational Research (EJOR), 113(2), 390-434. doi:<a href="https://doi.org/10.1016/S0377-2217(98">10.1016/S0377-2217(98)00113-1</a>00113-1). BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC482">JM1999DJSSPPAF</a></li>
+<dt id="JPD">JPD</dt><dd>Jorapur V, Puranik VS, Deshpande AS, Sharma MR (2014). â€œComparative Study of Different Representations in Genetic Algorithms for Job Shop Scheduling Problem.â€ Journal of Software Engineering and Applications (JSEA), 7(7), 571-580. doi:<a href="https://doi.org/10.4236/jsea.2014.77053">10.4236/jsea.2014.77053</a>, <a href="http://www.scirp.org/journal/paperinformation.aspx?paperid=46670">http://www.scirp.org/journal/paperinformation.aspx?paperid=46670</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC495">JPDS2014CAODRIGAFJSSP</a></li>
+<dt id="JZ">JZ</dt><dd>Jiang T, Zhang C (2018). â€œApplication of Grey Wolf Optimization for Solving Combinatorial Problems: Job Shop and Flexible Job Shop Scheduling Cases.â€ IEEE Access, 6, 26231-26240. doi:<a href="https://doi.org/10.1109/ACCESS.2018.2833552">10.1109/ACCESS.2018.2833552</a>, <a href="http://ieeexplore.ieee.org/document/8355479">http://ieeexplore.ieee.org/document/8355479</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC509">JZ2018AOGWOFSCPJSAFJSSC</a></li>
+<dt id="K">K</dt><dd>Kolonko M (1999). â€œSome New Results on Simulated Annealing Applied to the Job Shop Scheduling Problem.â€ European Journal of Operational Research (EJOR), 113(1), 123-136. doi:<a href="https://doi.org/10.1016/S0377-2217(97">10.1016/S0377-2217(97)00420-7</a>00420-7). BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC522">K1999SNROSAATTJSSP</a></li>
+<dt id="K2">K2</dt><dd>Kurdi M (2015). â€œA New Hybrid Island Model Genetic Algorithm for Job Shop Scheduling Problem.â€ Computers & Industrial Engineering, 88, 273-283. doi:<a href="https://doi.org/10.1016/j.cie.2015.07.015">10.1016/j.cie.2015.07.015</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC535">K2015ANHIMGAFJSSP</a></li>
+<dt id="KNF">KNF</dt><dd>Koshimura M, Nabeshima H, Fujita H, Hasegawa R (2010). â€œSolving Open Job-Shop Scheduling Problems by SAT Encoding.â€ IEICE Transactions on Information and Systems, E93.D(8), 2316-2318. doi:<a href="https://doi.org/10.1587/transinf.E93.D.2316">10.1587/transinf.E93.D.2316</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC547">KNFH2010SOJSSPBSE</a></li>
+<dt id="KV">KV</dt><dd>Kulkarni K, Venkateswaran J (2014). â€œIterative Simulation and Optimization Approach for Job Shop Scheduling.â€ In Buckley SJ, Miller JA (eds.), Proceedings of the 2014 Winter Simulation Conference, December 7-10, 2014, Savannah, GA, USA, 1620-1631. doi:<a href="https://doi.org/10.1109/WSC.2014.7020013">10.1109/WSC.2014.7020013</a>, <a href="https://www.anylogic.com/upload/iblock/5aa/5aa2987b839049668eeef8a21c811e6b.pdf">https://www.anylogic.com/upload/iblock/5aa/5aa2987b839049668eeef8a21c811e6b.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC559">KV2014ISAOAFJSS</a></li>
+<dt id="L">L</dt><dd>Lawrence SR (1984). Resource Constrained Project Scheduling: An Experimental Investigation of Heuristic Scheduling Techniques (Supplement). Ph.D. thesis, Graduate School of Industrial Administration (GSIA), Carnegie-Mellon University, Pittsburgh, PA, USA. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC572">L1998RCPSAEIOHSTS</a></li>
+<dt id="LWF">LWF</dt><dd>Li L, Weng W, Fujimura S (2017). â€œAn Improved Teaching-Learning-based Optimization Algorithm to Solve Job Shop Scheduling Problems.â€ In Zhu G, Yao S, Cui X, Xu S (eds.), 16th IEEE/ACIS International Conference on Computer and Information Science (ICIS'17), May 24-26, 2017, Wuhan, China, 797-801. ISBN 978-1-5090-5507-4, doi:<a href="https://doi.org/10.1109/ICIS.2017.7960101">10.1109/ICIS.2017.7960101</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC582">LWF2017AITLBOATSJSSP</a></li>
+<dt id="LYL">LYL</dt><dd>Liu M, Yao X, Li Y (2020). â€œHybrid Whale Optimization Algorithm Enhanced with LÃ©vy Flight and Differential Evolution for Job Shop Scheduling Problems.â€ Applied Soft Computing Journal (ASOC), 87, 105954. doi:<a href="https://doi.org/10.1016/j.asoc.2019.105954">10.1016/j.asoc.2019.105954</a>, Originally, the paper had two typos in the results. It reports an average result (918.4) for WSO-LFDE on la20, which is worse than the worst result (902) it reports. We therefore ignore the worst reported result for that algorithm on that instance, since it was probably accidentally copy-pasted from the best result. On instance la23, the lower bound is 1032 but the result 1023 is reported, which is clearly an accidental typo. These typos are currently fixed in an erratum process. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC594">LYL2020HWOAEWLFADEFJSSP</a></li>
+<dt id="M">M</dt><dd>Martin PD (1996). A Time-Oriented Approach to Computing Optimal Schedules for the Job-Shop Scheduling Problem. Ph.D. thesis, School of Operations Research and Industrial Engineering, Cornell University, Ithaca, NY, USA. oclc: 64683112. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC606">M1996ATOATCOSFTJSSP</a></li>
+<dt id="M2">M2</dt><dd>Mahapatra DK (2012). â€œBachelor's Thesis: Job Shop Scheduling using Artificial Immune System.â€ guided by Prof. S. S. Mahapatra, <a href="http://pdfs.semanticscholar.org/a350/070a2612d046d11feb33e64d1ab58cd8870d.pdf">http://pdfs.semanticscholar.org/a350/070a2612d046d11feb33e64d1ab58cd8870d.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC617">M2012JSSUAIS</a></li>
+<dt id="MF">MF</dt><dd>McMahon G, Florian M (1975). â€œOn Scheduling with Ready Times and Due Dates to Minimize Maximum Lateness.â€ Operations Research, 23(3), 475-482. doi:<a href="https://doi.org/10.1287/opre.23.3.475">10.1287/opre.23.3.475</a>, jstor: 169697. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC629">MF1975OSWRTADDTMML</a></li>
+<dt id="MHT">MHT</dt><dd>Mui NH, Hoa VD, Tuyen LT (2012). â€œA Parallel Genetic Algorithm for the Job Shop Scheduling Problem.â€ In Proceedings of the IEEE International Symposium on Signal Processing and Information Technology (ISSPIT'12), December 12-15, 2012, Ho Chi Minh City, Vietnam, 19-24. ISBN 978-1-4673-5604-6, doi:<a href="https://doi.org/10.1109/ISSPIT.2012.6621254">10.1109/ISSPIT.2012.6621254</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC643">MHT2012APGAFTJSSP</a></li>
+<dt id="MM">MM</dt><dd>MagalhÃ£es-Mendes J (2013). â€œA Comparative Study of Crossover Operators for Genetic Algorithms to Solve the Job Shop Scheduling Problem.â€ WSEAS Transactions on Computers, 12(4), 164-173. <a href="http://www.wseas.org/multimedia/journals/computers/2013/5705-156.pdf">http://www.wseas.org/multimedia/journals/computers/2013/5705-156.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC656">MM2013ACSOCOFGATSTJSSP</a></li>
+<dt id="MNK">MNK</dt><dd>Maqsood S, Noor S, Khan MK, Wood A (2012). â€œHybrid Genetic Algorithm (GA) for Job Shop Scheduling Problems and its Sensitivity Analysis.â€ International Journal of Intelligent Systems Technologies and Applications (IJISTA), 11(1/2), 49-62. doi:<a href="https://doi.org/10.1504/IJISTA.2012.046543">10.1504/IJISTA.2012.046543</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC669">MNKW2012HGAGFJSSPAISA</a></li>
+<dt id="MTS">MTS</dt><dd>Miller-Todd J, SteinhÃ¶fel K, Veenstra P (2018). â€œFirefly-Inspired Algorithm for Job Shop Scheduling.â€ In BÃ¶ckenhauer H, Komm D, Unger W (eds.), Adventures Between Lower Bounds and Higher Altitudes - Essays Dedicated to Juraj HromkoviÄ on the Occasion of His 60th Birthday, volume 11011 series Lecture Notes in Computer Science (LNCS), 423-433. Springer. ISBN 978-3-319-98354-7, doi:<a href="https://doi.org/10.1007/978-3-319-98355-4_24">10.1007/978-3-319-98355-4_24</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC681">MTSV2018FIAFJSS</a></li>
+<dt id="N">N</dt><dd>Nazif H (2015). â€œSolving Job Shop Scheduling Problem Using an Ant Colony Algorithm.â€ Journal of Asian Scientific Research, 5(5), 261-268. doi:<a href="https://doi.org/10.18488/journal.2/2015.5.5/2.5.261.268">10.18488/journal.2/2015.5.5/2.5.261.268</a>, <a href="http://www.aessweb.com/pdf-files/jasr-2015-5(5)-261-268.pdf">http://www.aessweb.com/pdf-files/jasr-2015-5(5)-261-268.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC696">N2015SJSSPUAACO</a></li>
+<dt id="NA">NA</dt><dd>Narendhar S, Amudha T (2012). â€œA Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems.â€ International Journal of Programming Languages and Applications (IJPLA), 2(4), 1-11. doi:<a href="https://doi.org/10.5121/ijpla.2012.2401">10.5121/ijpla.2012.2401</a>, Also available via Computing Research Repository (CoRR) abs/1211.4971 at arXiv:1211.4971v1 [cs.NE], <a href="https://arxiv.org/pdf/1211.4971.pdf">https://arxiv.org/pdf/1211.4971.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC709">NA2012AHBFAFSJSSP</a></li>
+<dt id="NS">NS</dt><dd>Nowicki E, Smutnicki C (1996). â€œA Fast Taboo Search Algorithm for the Job Shop Problem.â€ Management Science, 42(6), 783-938. doi:<a href="https://doi.org/10.1287/mnsc.42.6.797">10.1287/mnsc.42.6.797</a>, jstor: 2634595, <a href="http://pacciarelli.inf.uniroma3.it/CORSI/MSP/NowickiSmutnicki96.pdf">http://pacciarelli.inf.uniroma3.it/CORSI/MSP/NowickiSmutnicki96.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC724">NS1996AFTSAFTJSP</a></li>
+<dt id="NS2">NS2</dt><dd>Nowicki E, Smutnicki C (2005). â€œAn Advanced Taboo Search Algorithm for the Job Shop Problem.â€ Journal of Scheduling, 8(2), 145-159. doi:<a href="https://doi.org/10.1007/s10951-005-6364-5">10.1007/s10951-005-6364-5</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC739">NS2005AATSAFTJSP</a></li>
+<dt id="NZJ">NZJ</dt><dd>Nguyen S, Zhang M, Johnston M, Tan KC (2013). â€œA Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem.â€ IEEE Transactions on Evolutionary Computation (TEVC), 17(5), 621-639. doi:<a href="https://doi.org/10.1109/TEVC.2012.2227326">10.1109/TEVC.2012.2227326</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC752">NZJT2013ACSORIGPTED</a></li>
+<dt id="ODP">ODP</dt><dd>Oliveira JA, Dias L, Pereira G (2010). â€œSolving the Job Shop Problem with a Random Keys Genetic Algorithm with Instance Parameters.â€ In Rodrigues H, Herskovits J, Soares CM, Guedes JM, Folgado J, AraÃºjo A, Moleiro F, Kuzhichalil JP, Madeira JA, DimitrovovÃ¡ Z (eds.), Proceedings of the 2nd International Conference on Engineering Optimization (EngOpt2010), September 6-9, 2010, Lisbon, Portugal. ISBN 978-989-96264-3-0, <a href="http://www1.dem.ist.utl.pt/engopt2010/Book_and_CD/Papers_CD_Final_Version/pdf/08/01512-01.pdf">http://www1.dem.ist.utl.pt/engopt2010/Book_and_CD/Papers_CD_Final_Version/pdf/08/01512-01.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC765">ODP2010STJSPWARKGAWIP</a></li>
+<dt id="OV">OV</dt><dd>Ombuki BM, Ventresca M (2004). â€œLocal Search Genetic Algorithms for the Job Shop Scheduling Problem.â€ Applied Intelligence - The International Journal of Research on Intelligent Systems for Real Life Complex Problems, 21(1), 99-109. doi:<a href="https://doi.org/10.1023/B:APIN.0000027769.48098.91">10.1023/B:APIN.0000027769.48098.91</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC779">OV2004LSGAFTJSSP</a></li>
+<dt id="P">P</dt><dd>Pongchairerks P (2014). â€œVariable Neighbourhood Search Algorithms Applied to Job-Shop Scheduling Problems.â€ International Journal of Mathematics in Operational Research (IJMOR), 6(6), 752-774. doi:<a href="https://doi.org/10.1504/IJMOR.2014.065421">10.1504/IJMOR.2014.065421</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC792">P2014VNSAATJSSP</a></li>
+<dt id="P2">P2</dt><dd>Pongchairerks P (2019). â€œA Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem.â€ Complexity, 2019(8683472), 1-11. doi:<a href="https://doi.org/10.1155/2019/8683472">10.1155/2019/8683472</a>, <a href="http://www.hindawi.com/journals/complexity/2019/8683472/">http://www.hindawi.com/journals/complexity/2019/8683472/</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC804">P2019ATLMAFTJSSP</a></li>
+<dt id="PLC">PLC</dt><dd>Peng B, LÃ¼ Z, Cheng TCE (2015). â€œA Tabu Search/Path Relinking Algorithm to Solve the Job Shop Scheduling Problem.â€ Computers & Operations Research, 53, 154-164. doi:<a href="https://doi.org/10.1016/j.cor.2014.08.006">10.1016/j.cor.2014.08.006</a>, A February 2014 preprint is available as arXiv:1402.5613v1 [cs.DS], <a href="http://arxiv.org/abs/1402.5613">http://arxiv.org/abs/1402.5613</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC817">PLC2015ATSPRATSTJSSP</a></li>
+<dt id="PM">PM</dt><dd>Pezzella F, Merelli E (2000). â€œA Tabu Search Method Guided by Shifting Bottleneck for the Job Shop Scheduling Problem.â€ European Journal of Operational Research (EJOR), 120(2), 297-310. doi:<a href="https://doi.org/10.1016/S0377-2217(99">10.1016/S0377-2217(99)00158-7</a>00158-7), <a href="https://www2.cs.sfu.ca/CourseCentral/827/havens/papers/topic%2310(JobShop)/Tabu%20With%20Shifting.pdf">https://www2.cs.sfu.ca/CourseCentral/827/havens/papers/topic%2310(JobShop)/Tabu%20With%20Shifting.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC831">PM2000ATSMGBSBFTJSSP</a></li>
+<dt id="PPH">PPH</dt><dd>PÃ©rez E, Posada M, Herrera F (2012). â€œAnalysis of New Niching Genetic Algorithms for Finding Multiple Solutions in the Job Shop Scheduling.â€ Journal of Intelligent Manufacturing, 23(3), 341-356. doi:<a href="https://doi.org/10.1007/s10845-010-0385-4">10.1007/s10845-010-0385-4</a>, reports result 595.97 for la03, which is below the lower bound of 597 and thus not included in our data set. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC845">PPH2012AONNGAFMSITJSS</a></li>
+<dt id="PSV">PSV</dt><dd>Pardalos PM, Shylo OV, Vazacopoulos A (2010). â€œSolving Job Shop Scheduling Problems Utilizing the Properties of Backbone and "Big Valley".â€ Computational Optimization and Applications, 47(1), 61-76. doi:<a href="https://doi.org/10.1007/s10589-008-9206-5">10.1007/s10589-008-9206-5</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC859">PSV2010SJSSPUTPOBABV</a></li>
+<dt id="QL">QL</dt><dd>Qiu X, Lau HYK (2014). â€œAn AIS-based Hybrid Algorithm for Static Job Shop Scheduling Problem.â€ Journal of Intelligent Manufacturing, 25(3), 489-503. doi:<a href="https://doi.org/10.1007/s10845-012-0701-2">10.1007/s10845-012-0701-2</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC872">QL2014AABHAFSSSP</a></li>
+<dt id="RNK">RNK</dt><dd>Raeesi N. MR, Kobti Z (2012). â€œA Knowledge-Migration-Based Multi-Population Cultural Algorithm to Solve Job Shop Scheduling.â€ In Youngblood GM, McCarthy PM (eds.), Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference (FLAIRS'12), May 23-25, 2012, Marco Island, FL, USA. ISBN 978-1-57735-558-8, <a href="http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS12/paper/view/4378/4768">http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS12/paper/view/4378/4768</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC885">RNK2012AKMBMPCATSJSS</a></li>
+<dt id="S">S</dt><dd>Schilham R (2000). â€œResults listed on Ã‰ric Taillard's Page.â€ see also http://jobshop.jjvh.nl/, <a href="http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html">http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC907">S200RLOETP</a></li>
+<dt id="S2">S2</dt><dd>Shylo OV (2019). â€œJob Shop Scheduling (Personal Homepage).â€ <a href="http://optimizizer.com/jobshop.php">http://optimizizer.com/jobshop.php</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC897">S2019JSSPH</a></li>
+<dt id="SB">SB</dt><dd>SabuncuoÄŸlu Ä°, Bayiz M (1999). â€œJob Shop Scheduling with Beam Search.â€ European Journal of Operational Research (EJOR), 118(2), 390-412. doi:<a href="https://doi.org/10.1016/S0377-2217(98">10.1016/S0377-2217(98)00319-1</a>00319-1), <a href="http://yoksis.bilkent.edu.tr/doi_getpdf/articles/10.1016-S0377-2217(98)00319-1.pdf">http://yoksis.bilkent.edu.tr/doi_getpdf/articles/10.1016-S0377-2217(98)00319-1.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC916">SB1999JSSWBS</a></li>
+<dt id="SIS">SIS</dt><dd>Shi G, Iima H, Sannomiya N (1997). â€œNew Encoding Scheme for Solving Job Shop Problems by Genetic Algorithm.â€ In Proceedings of the 35th IEEE Conference on Decision and Control (CDC'96), December 11-13, 1996, Kobe, Japan, volume 4, 4395-4400. ISBN 0-7803-3590-2, doi:<a href="https://doi.org/10.1109/CDC.1996.577484">10.1109/CDC.1996.577484</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC930">SIS1997NESFSJSPBGA</a></li>
+<dt id="SK">SK</dt><dd>Sakuma J, Kobayashi S (2000). â€œExtrapolation-Directed Crossover for Job-Shop Scheduling Problems: Complementary Combination with JOX.â€ In Whitley LD, Goldberg DE, CantÃº-Paz E, Spector L, Parmee IC, Beyer H (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'00), July 8-12, 2000, Las Vegas, NV, USA, 973-980. ISBN 1-55860-708-0. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC943">SK2000EDCFJSSPCCWJ</a></li>
+<dt id="SMM">SMM</dt><dd>Sahana SK, Mukherjee I, Mahanti PK (2018). â€œParallel Artificial Bee Colony (PABC) for Job Shop Scheduling Problems.â€ Advances in Information Sciences and Service Sciences (AISS), 10(3), 1-11. reports 661 as result for abz9 which is below the lower bound 678 and thus not included in our data set, <a href="http://www.globalcis.org/aiss/ppl/AISS3877PPL.pdf">http://www.globalcis.org/aiss/ppl/AISS3877PPL.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC955">SMM2018PABCPFJSSP</a></li>
+<dt id="SS">SS</dt><dd>Shylo OV, Shams H (2018). â€œBoosting Binary Optimization via Binary Classification: A Case Study of Job Shop Scheduling.â€ cs.AI/math.OC abs/1808.10813, arXiv. Many results are available in the GitHub repository https://github.com/quasiquasar/gta-jobshop-data. We just use a subset (namely, samples after 3, 5, 30, and 60 minutes, and the end results) to compute statistics. The paper reports some new bks for which the creating runs are not contained in the GitHub repository, verified via email with the authors, as well as bound 6196 for both dmu74 and dmu75. Other results have been published on Prof. Shylo's website http://optimizizer.com/DMU.php for the same paper (including dmu17), <a href="https://arxiv.org/pdf/1808.10813">https://arxiv.org/pdf/1808.10813</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC969">SS2018BBOVBCACSOJSS</a></li>
+<dt id="SSS">SSS</dt><dd>Sharma N, Sharma H, Sharma A (2018). â€œBeer Froth Artificial Bee Colony Algorithm for Job-Shop Scheduling Problem.â€ Applied Soft Computing Journal (ASOC), 68, 507-524. doi:<a href="https://doi.org/10.1016/j.asoc.2018.04.001">10.1016/j.asoc.2018.04.001</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC982">SSS2018BFABCAFJSSP</a></li>
+<dt id="SWV">SWV</dt><dd>Storer RH, Wu SD, Vaccari R (1992). â€œNew Search Spaces for Sequencing Problems with Application to Job Shop Scheduling.â€ Management Science, 38(10), 1495-1509. doi:<a href="https://doi.org/10.1287/mnsc.38.10.1495">10.1287/mnsc.38.10.1495</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC993">SWV1992NSSFSPWATJSS</a></li>
+<dt id="T">T</dt><dd>Taillard Ã‰D (1993). â€œBenchmarks for Basic Scheduling Problems.â€ European Journal of Operational Research (EJOR), 64(2), 278-285. doi:<a href="https://doi.org/10.1016/0377-2217(93">10.1016/0377-2217(93)90182-M</a>90182-M). BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1005">T199BFBSP</a></li>
+<dt id="V">V</dt><dd>Vaessens RJM (1995). â€œResults listed on Ã‰ric Taillard's Page.â€ see also http://jobshop.jjvh.nl/, <a href="http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html">http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1027">V1995RLOETP</a></li>
+<dt id="V1">V1</dt><dd>Vaessens RJM (1996). â€œAddition to John Edward Beasley's OR Library.â€ see also http://jobshop.jjvh.nl/, <a href="http://people.brunel.ac.uk/~mastjjb/jeb/orlib/files/jobshop1.txt">http://people.brunel.ac.uk/~mastjjb/jeb/orlib/files/jobshop1.txt</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1018">V1996ATJEBOL</a></li>
+<dt id="VAL">VAL</dt><dd>Vaessens RJM, Aarts EHL, Lenstra JK (1996). â€œJob Shop Scheduling by Local Search.â€ INFORMS Journal on Computing, 8(3), 302-317. doi:<a href="https://doi.org/10.1287/ijoc.8.3.302">10.1287/ijoc.8.3.302</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1036">VAL1996JSSBLS</a></li>
+<dt id="vH">vH</dt><dd>van Hoorn JJ (2015). â€œJob Shop Instances and Solutions.â€ <a href="http://jobshop.jjvh.nl">http://jobshop.jjvh.nl</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1049">vH2015JSIAS</a></li>
+<dt id="vH2">vH2</dt><dd>van Hoorn JJ (2016). Dynamic Programming for Routing and Scheduling: Optimizing Sequences of Decisions. Ph.D. thesis, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. <a href="http://jobshop.jjvh.nl/dissertation">http://jobshop.jjvh.nl/dissertation</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1057">vH2016DPFRASOSOD</a></li>
+<dt id="VLS">VLS</dt><dd>VilÃm P, Laborie P, Shaw P (2015). â€œFailure-Directed Search for Constraint-Based Scheduling.â€ In Michel L (ed.), International Conference Integration of AI and OR Techniques in Constraint Programming: Proceedings of 12th International Conference on AI and OR Techniques in Constriant Programming for Combinatorial Optimization Problems (CPAIOR'2015), May 18-22, 2015, Barcelona, Spain, volume 9075 series Lecture Notes in Computer Science (LNCS) and Theoretical Computer Science and General Issues book sub series (LNTCS), 437-453. ISBN 978-3-319-18007-6, doi:<a href="https://doi.org/10.1007/978-3-319-18008-3_30">10.1007/978-3-319-18008-3_30</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1070">VLS2015FDSFCBS</a></li>
+<dt id="VLS2">VLS2</dt><dd>VilÃm P, Laborie P, Shaw P (2015). â€œFailure-Directed Search for Constraint-Based Scheduling - Detailed Experimental Results.â€ The detailed experimental results of the paper "Failure-Directed Search for Constraint-Based Scheduling" by the same authors, in International Conference Integration of AI and OR Techniques in Constraint Programming: Proceedings of 12th International Conference on AI and OR Techniques in Constriant Programming for Combinatorial Optimization Problems (CPAIOR'2015), May 18-22, 2015, Barcelona, Spain, pages 437-453, doi:10.1007/978-3-319-18008-3_30., <a href="http://vilim.eu/petr/cpaior2015-results.pdf">http://vilim.eu/petr/cpaior2015-results.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1086">VLS2015FDSFCBSDER</a></li>
+<dt id="W">W</dt><dd>Weise T (2019-2020). â€œjsspInstancesAndResults: Results, Data, and Instances of the Job Shop Scheduling Problem.â€ A GitHub repository with the common benchmark instances for the Job Shop Scheduling Problem as well as results from the literature, both in form of CSV files as well as R program code to access them., <a href="https://github.com/thomasWeise/jsspInstancesAndResults">https://github.com/thomasWeise/jsspInstancesAndResults</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1096">W2019JRDAIOTJSSP</a></li>
+<dt id="WCL">WCL</dt><dd>Wang L, Cai J, Li M (2016). â€œAn Adaptive Multi-Population Genetic Algorithm for Job-Shop Scheduling Problem.â€ Advances in Manufacturing, 4(2), 142-149. doi:<a href="https://doi.org/10.1007/s40436-016-0140-y">10.1007/s40436-016-0140-y</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1107">WCL2016AAMPGAFJSSP</a></li>
+<dt id="WD">WD</dt><dd>Wang X, Duan H (2014). â€œA Hybrid Biogeography-based Optimization Algorithm for Job Shop Scheduling Problem.â€ Computers & Industrial Engineering, 73, 96-114. doi:<a href="https://doi.org/10.1016/j.cie.2014.04.006">10.1016/j.cie.2014.04.006</a>, <a href="http://hbduan.buaa.edu.cn/papers/2014CAIE_Wang_Duan.pdf">http://hbduan.buaa.edu.cn/papers/2014CAIE_Wang_Duan.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1120">WD2014AHBBOAFJSSP</a></li>
+<dt id="WGK">WGK</dt><dd>Weckman GR, Ganduri CV, Koonce DA (2008). â€œA Neural Network Job-Shop Scheduler.â€ Journal of Intelligent Manufacturing, 19, 191-201. doi:<a href="https://doi.org/10.1007/s10845-008-0073-9">10.1007/s10845-008-0073-9</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1133">WGK2008ANNJSS</a></li>
+<dt id="WTC">WTC</dt><dd>Wang S, Tsai C, Chiang M (2018). â€œA High Performance Search Algorithm for Job-Shop Scheduling Problem.â€ In Shakshuki EM, Yasar A (eds.), The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN'18) / The 8th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH'18) / Affiliated Workshops, November 5-8, 2018, Leuven, Belgium, volume 141 series Procedia Computer Science, 119-126. doi:<a href="https://doi.org/10.1016/j.procs.2018.10.157">10.1016/j.procs.2018.10.157</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1146">WTC2018AHPSAFJSSP</a></li>
+<dt id="YN">YN</dt><dd>Yamada T, Nakano R (1992). â€œA Genetic Algorithm Applicable to Large-Scale Job-Shop Instances.â€ In MÃ¤nner R, Manderick B (eds.), Proceedings of Parallel Problem Solving from Nature 2 (PPSN II), September 28-30, 1992, Brussels, Belgium, 281-290. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1160">YN1992AGAATLSJSI</a></li>
+<dt id="YN1">YN1</dt><dd>Yamada T, Nakano R (1997). â€œGenetic Algorithms for Job-Shop Scheduling Problems.â€ In Proceedings of Modern Heuristic for Decision Support, March18-19, 1997, London, England, UK, 67-81. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1172">YN1997GAFJSSP</a></li>
+<dt id="ZHZ">ZHZ</dt><dd>Zupan H, HerakoviÄ N, Å½erovnik J (2016). â€œA Heuristic for the Job Shop Scheduling Problem.â€ In Papa G, Mernik M (eds.), The 7th International Conference on Bioinspired Optimization Methods and their Application (BIOMA'16), May 18-20, 2016, Bled, Slovenia, 187-198. ISBN 978-961-264-093-4, <a href="http://bioma.ijs.si/conference/BIOMA2016Proceedings.pdf">http://bioma.ijs.si/conference/BIOMA2016Proceedings.pdf</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1182">ZHZ2016AHFTJSSP</a></li>
+<dt id="ZLR">ZLR</dt><dd>Zhang C, Li P, Rao Y, Guan Z (2008). â€œA Very Fast TS/SA Algorithm for the Job Shop Scheduling Problem.â€ Computers & Operations Research, 35(1), 282-294. doi:<a href="https://doi.org/10.1016/j.cor.2006.02.024">10.1016/j.cor.2006.02.024</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1197">ZLRG2008AVFTAFTJSSP</a></li>
+<dt id="ZRL">ZRL</dt><dd>Zhang C, Rao Y, Li P (2008). â€œAn Effective Hybrid Genetic Algorithm for the Job Shop Scheduling Problem.â€ International Journal of Advanced Manufacturing Technology (JAMT), 39, 965-974. doi:<a href="https://doi.org/10.1007/s00170-007-1354-8">10.1007/s00170-007-1354-8</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1210">ZRL2008AEHGAFTJSSP</a></li>
+<dt id="ZSR">ZSR</dt><dd>Zhang C, Shao X, Rao Y, Qiu H (2008). â€œSome New Results on Tabu Search Algorithm Applied to the Job-Shop Scheduling Problem.â€ In Jaziri W (ed.), Tabu Search. IntechOpen, London, England, UK. ISBN 978-3-902613-34-9, doi:<a href="https://doi.org/10.5772/5593">10.5772/5593</a>, <a href="http://www.intechopen.com/books/tabu_search/some_new_results_on_tabu_search_algorithm_applied_to_the_job-shop_scheduling_problem">http://www.intechopen.com/books/tabu_search/some_new_results_on_tabu_search_algorithm_applied_to_the_job-shop_scheduling_problem</a>. BibTeX:<a href="data-raw/bibliography/bibliography.bib#LC1222">ZSRQ2008SNROTSAATTJSSP</a></li>
+</dl>
\ No newline at end of file
diff --git a/docs/source/related/kemmerling.md b/docs/source/related/kemmerling.md
new file mode 100644
index 0000000..7634faf
--- /dev/null
+++ b/docs/source/related/kemmerling.md
@@ -0,0 +1,6 @@
+# JSP Entropy Dataset 
+
+The dataset by [Kemmerling et al.](https://preprints.inggrid.org/repository/view/21/) provides an important benchmark 
+for job shop scheduling by explicitly addressing the diversity of jobs and operations using Shannon entropy. 
+Unlike traditional datasets that rely on randomization or difficulty maximization, this dataset systematically varies 
+instance diversity, making it particularly valuable for studying the impact of product mix on scheduling performance. 
diff --git a/docs/source/related/related-projects.md b/docs/source/related/related-projects.md
new file mode 100644
index 0000000..d255109
--- /dev/null
+++ b/docs/source/related/related-projects.md
@@ -0,0 +1,12 @@
+# Related Projects
+
+The following projects contain futher JSP instances or are equivalent native formats for other programming languages.
+
+```{toctree}
+:caption: 'Contents:'
+:maxdepth: 2
+
+kemmerling
+weise
+ruby
+```
\ No newline at end of file
diff --git a/docs/source/related/ruby.md b/docs/source/related/ruby.md
new file mode 100644
index 0000000..baa3f19
--- /dev/null
+++ b/docs/source/related/ruby.md
@@ -0,0 +1,3 @@
+# Ruby Equivalent
+
+A equivalent repository for the Ruby programming language is available in the repository [JSPLIB](https://github.com/tamy0612/JSPLIB).
diff --git a/docs/source/related/weise.md b/docs/source/related/weise.md
new file mode 100644
index 0000000..a0ceb2a
--- /dev/null
+++ b/docs/source/related/weise.md
@@ -0,0 +1,4 @@
+# R Equivalent
+
+Thomas Weise compiled a equivalent repository for the R programming language. 
+The repository is available at [here](https://github.com/thomasWeise/jsspInstancesAndResults).
diff --git a/docs/source/source-code-docs/instance-download.md b/docs/source/source-code-docs/instance-download.md
new file mode 100644
index 0000000..348eef1
--- /dev/null
+++ b/docs/source/source-code-docs/instance-download.md
@@ -0,0 +1,32 @@
+# Scraper Code
+
+The function below were used to scrape the content from [jjvh.nl](http://jobshop.jjvh.nl/) and convert them into a python files.
+The purpose of this scraper is automate the process of fetching JSP instances from the website and convert them into a 
+format that can be imported as a python package.
+
+```{warning}
+[jjvh.nl](http://jobshop.jjvh.nl/) is frequently down. If the website is down, the scraper will not work.
+```
+
+```{tip}
+All python file in the package '_download_instances_to_project' have a main function that can be executed to showcase 
+the functionality of the file and its functions.
+```
+
+
+```{eval-rst} 
+.. autofunction:: _download_instances_to_project.downloader::download_instances
+
+```
+
+```{eval-rst} 
+.. automodule:: _download_instances_to_project.instance_details_downloader
+   :members:
+
+```
+
+```{eval-rst} 
+.. automodule:: _download_instances_to_project.python_file_writer
+   :members:
+
+```
\ No newline at end of file
diff --git a/docs/source/source-code-docs/instance-generator.md b/docs/source/source-code-docs/instance-generator.md
new file mode 100644
index 0000000..11723c1
--- /dev/null
+++ b/docs/source/source-code-docs/instance-generator.md
@@ -0,0 +1,10 @@
+# Instance Generator
+
+The instance generator is a tool that generates JSP instances. 
+The instances are generated based on the parameters provided to the generator function.
+
+```{eval-rst} 
+.. automodule:: jsp_instance_utils.jsp_instance_generator
+   :members:
+
+```
diff --git a/docs/source/source-code-docs/or-tools-solver.md b/docs/source/source-code-docs/or-tools-solver.md
new file mode 100644
index 0000000..aa02a86
--- /dev/null
+++ b/docs/source/source-code-docs/or-tools-solver.md
@@ -0,0 +1,11 @@
+# Exact Solver
+
+The function {py:func}`jsp_instance_utils.jsp_or_tools_solver` is an exact solver for JSP instances that takes JSP instances in the format of this library.
+The function uses the Google OR-Tools library to solve the instances and is based on [this](https://developers.google.com/optimization/scheduling/job_shop) example from the OR-Tools documentation.
+
+```{eval-rst} 
+.. automodule:: jsp_instance_utils.jsp_or_tools_solver
+   :members:
+
+```
+
diff --git a/docs/source/source-code-docs/parser.md b/docs/source/source-code-docs/parser.md
new file mode 100644
index 0000000..86c45d4
--- /dev/null
+++ b/docs/source/source-code-docs/parser.md
@@ -0,0 +1,28 @@
+# Parser
+
+The parser module contains functions to parse job shop scheduling instances from text files into NumPy arrays. The module also contains functions to convert the NumPy arrays.
+
+Two commonly used formats for job shop scheduling instances are the standard format and the talliard format. 
+The standard format is a text file with the following structure:
+```{eval-rst}
+.. literalinclude:: ../_static/ft06-std.txt
+   :language: none
+   :caption: The `ft06` instance in standard format (as a .txt file).
+```
+The exact format of talliard instances is explained in {py:func}`jsp_instance_utils.instance_parser.parse_jps_standard_specification`.
+
+The talliard format is a text file with the following structure:
+```{eval-rst}
+.. literalinclude:: ../_static/ft06-ta.txt
+   :language: none
+   :caption: The `ft06` instance in talliard format (as a .txt file).
+```
+
+The exact format of talliard instances is explained in {py:func}`jsp_instance_utils.instance_parser.parse_jps_taillard_specification`.
+
+
+```{eval-rst} 
+.. automodule:: jsp_instance_utils.instance_parser
+   :members:
+
+```
diff --git a/docs/source/source-code-docs/source-code.md b/docs/source/source-code-docs/source-code.md
new file mode 100644
index 0000000..5efe5f9
--- /dev/null
+++ b/docs/source/source-code-docs/source-code.md
@@ -0,0 +1,14 @@
+# Source Code
+
+The source code for the JSP environment.
+
+```{toctree}
+:caption: 'Contents:'
+:maxdepth: 2
+
+
+parser
+instance-generator
+or-tools-solver
+instance-download
+```
\ No newline at end of file
diff --git a/examples/JSSEnv_example.py b/examples/JSSEnv_example.py
index e69de29..cd16c96 100644
--- a/examples/JSSEnv_example.py
+++ b/examples/JSSEnv_example.py
@@ -0,0 +1,47 @@
+import numpy as np
+from JSSEnv.envs import JssEnv
+
+# in this example we create the instance file instead of downloading it, because https://jobshop.jjvh.nl/ is
+# sometimes down. You can also download the instance from https://jobshop.jjvh.nl/ or use the download utils of
+# the package.
+#
+# Note: this repo also contains .txt files with instances from https://jobshop.jjvh.nl/ in the resources folder. So that
+#       is an alternative to downloading the instances from the website.
+
+
+# setup the jsp instance
+instance_text = """6	6 
+2	1	0	3	1	6	3	7	5	3	4	6
+1	8	2	5	4	10	5	10	0	10	3	4
+2	5	3	4	5	8	0	9	1	1	4	7
+1	5	0	5	2	5	3	3	4	8	5	9
+2	9	1	3	4	5	5	4	0	3	3	1
+1	3	3	3	5	9	0	10	4	4	2	1
+"""
+jsp_std_path = "ft06.txt"
+# write the instance to a file
+with open(jsp_std_path, "w") as f:
+    f.write(instance_text)
+
+# create the environment
+env = JssEnv(env_config={'instance_path': jsp_std_path})
+
+# reset the environment
+obs = env.reset()
+
+# perform a random action till the environment is done
+done = False
+while not done:
+    # sample a random action
+    mask = env.unwrapped.legal_actions.astype(np.int8)
+    action = env.action_space.sample(mask=mask)
+    # take a step in the environment
+    obs, reward, done, info = env.step(action)
+
+    # render the environment
+
+env.render()
+print(f"makespan: {env.unwrapped.last_time_step}")
+
+if __name__ == '__main__':
+    pass
diff --git a/examples/graph-matrix-jsp-env_example.py b/examples/graph-matrix-jsp-env_example.py
index e69de29..b19cfe4 100644
--- a/examples/graph-matrix-jsp-env_example.py
+++ b/examples/graph-matrix-jsp-env_example.py
@@ -0,0 +1,29 @@
+from jsp_instance_utils.instances import ft06, ft06_makespan
+from graph_matrix_jsp_env.disjunctive_jsp_env import DisjunctiveGraphJspEnv
+import numpy as np
+
+
+def main():
+    env = DisjunctiveGraphJspEnv(
+        jsp_instance=ft06,
+        c_lb=ft06_makespan,
+        reward_function="makespan"
+    )
+
+    done = False
+    info = {}
+    while not done:
+        # get valid action mask. sample expects it to be a numpy array of type int8
+        mask = np.array(env.valid_action_mask()).astype(np.int8)
+        action = env.action_space.sample(mask=mask)
+        state, reward, terminated, truncated, info = env.step(action)
+
+        done = terminated or truncated
+
+    # console rendering
+    env.render()
+    print(f"makespan: {env.get_makespan()}")
+
+
+if __name__ == '__main__':
+    main()
diff --git a/examples/kemmerling_example.py b/examples/kemmerling_example.py
deleted file mode 100644
index e69de29..0000000
diff --git a/examples/or-tools_example.py b/examples/or-tools_example.py
index e69de29..201577a 100644
--- a/examples/or-tools_example.py
+++ b/examples/or-tools_example.py
@@ -0,0 +1,12 @@
+from jsp_instance_utils.instances import ft06, ft06_makespan
+from jsp_instance_utils.jsp_or_tools_solver import solve_jsp
+
+makespan, status, *_ = solve_jsp(jsp_instance=ft06, plot_results=True)
+
+assert status == "OPTIMAL"
+assert makespan == ft06_makespan
+
+print(f"makespan: {makespan} ({status} solution)")
+
+if __name__ == '__main__':
+    pass
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index d517169..4e75612 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,15 +36,18 @@ dev = [
     "gymnasium",
     "JSSEnv",
     "graph-jsp-env",
+    "graph-matrix-jsp-env",
     # documentation
     "sphinx",
     "myst-parser", # .md support for sphinx
     "sphinx-autobuild",
+    "nbsphinx", # for jupyter notebook support in sphinx
     #
     "furo", # cool theme
     "twine",
     "sphinx-copybutton", # for code copy buttons
     "nbsphinx", # for jupyter notebook support in sphinx
+    "pandoc",
     # for converting examples to notebooks
     "jupytext",
     "notebook",
diff --git a/requirements_dev.txt b/requirements_dev.txt
index bf7b28d..e8620fd 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -117,6 +117,8 @@ furo==2024.8.6
     # via jsp-instance-utils (pyproject.toml)
 graph-jsp-env==1.0.0
     # via jsp-instance-utils (pyproject.toml)
+graph-matrix-jsp-env==0.3.2
+    # via jsp-instance-utils (pyproject.toml)
 gym==0.26.2
     # via
     #   jsp-instance-utils (pyproject.toml)
@@ -126,6 +128,7 @@ gym-notices==0.0.8
 gymnasium==1.1.1
     # via
     #   graph-jsp-env
+    #   graph-matrix-jsp-env
     #   jsp-instance-utils (pyproject.toml)
 h11==0.14.0
     # via
@@ -190,7 +193,9 @@ jsonschema[format-nongpl]==4.23.0
 jsonschema-specifications==2024.10.1
     # via jsonschema
 jsp-vis==1.1.0
-    # via jsp-instance-utils (pyproject.toml)
+    # via
+    #   graph-matrix-jsp-env
+    #   jsp-instance-utils (pyproject.toml)
 jssenv==1.0.2
     # via jsp-instance-utils (pyproject.toml)
 jupyter-client==8.6.3
@@ -252,6 +257,7 @@ markupsafe==3.0.2
 matplotlib==3.8.4
     # via
     #   graph-jsp-env
+    #   graph-matrix-jsp-env
     #   jsp-vis
 matplotlib-inline==0.1.7
     # via
@@ -304,6 +310,7 @@ numpy==1.26.4
     # via
     #   contourpy
     #   graph-jsp-env
+    #   graph-matrix-jsp-env
     #   gym
     #   gymnasium
     #   imageio
@@ -344,6 +351,8 @@ pandas==2.2.2
     #   jsp-vis
     #   jssenv
     #   ortools
+pandoc==2.4
+    # via jsp-instance-utils (pyproject.toml)
 pandocfilters==1.5.1
     # via nbconvert
 parso==0.8.4
@@ -370,6 +379,10 @@ pluggy==1.4.0
     # via
     #   pytest
     #   tox
+plumbum==1.9.0
+    # via pandoc
+ply==3.11
+    # via pandoc
 prometheus-client==0.21.1
     # via jupyter-server
 prompt-toolkit==3.0.50
@@ -419,7 +432,9 @@ python-json-logger==3.3.0
 pytz==2024.1
     # via pandas
 pywin32==310
-    # via jupyter-core
+    # via
+    #   jupyter-core
+    #   plumbum
 pywin32-ctypes==0.2.3
     # via keyring
 pywinpty==2.0.15
@@ -468,6 +483,7 @@ rfc3986-validator==0.1.1
 rich==13.7.1
     # via
     #   graph-jsp-env
+    #   graph-matrix-jsp-env
     #   twine
 rpds-py==0.23.1
     # via
diff --git a/resources/bibliography.bib b/resources/bibliography.bib
new file mode 100644
index 0000000..eabe01a
--- /dev/null
+++ b/resources/bibliography.bib
@@ -0,0 +1,1236 @@
+% original file : https://raw.githubusercontent.com/thomasWeise/jsspInstancesAndResults/master/data-raw/bibliography/bibliography.bib
+
+@string { jan = "{January}" }
+@string { feb = "{February}" }
+@string { mar = "{March}" }
+@string { apr = "{April}" }
+@string { may = "{May}" }
+@string { jun = "{June}" }
+@string { jul = "{July}" }
+@string { aug = "{August}" }
+@string { sep = "{September}" }
+@string { oct = "{October}" }
+@string { nov = "{November}" }
+@string { dec = "{December}" }
+@string { spring = "{Spring}" }
+@string { summer = "{Summer}" }
+@string { autumn = "{Autumn}" }
+@string { winter = "{Winter}" }
+
+@string { aaiaij = "{Applied Artificial Intelligence -- An International Journal}" }
+@string { ai = "{Applied Intelligence -- The International Journal of Research on Intelligent Systems for Real Life Complex Problems}" }
+@string { aim = "{Advances in Manufacturing}" }
+@string { aiss = "{Advances in Information Sciences and Service Sciences {(AISS)}}" }
+@string { aor = "{Annals of Operations Research}" }
+@string { asoc = "{Applied Soft Computing Journal ({ASOC})}" }
+@string { caie = "{Computers {\&} Industrial Engineering}" }
+@string { caor = "{Computers {\&} Operations Research}" }
+@string { compl = "{Complexity}" }
+@string { coaa = "{Computational Optimization and Applications}" }
+@string { ejor = "{European Journal of Operational Research ({EJOR})}" }
+@string { enidm = "{Electronic Notes in Discrete Mathematics}" }
+@string { ia = "{{IEEE} Access}" }
+@string { ijcopi = "{International Journal of Combinatorial Optimization Problems and Informatics ({IJCOPI})}" }
+@string { ijmor = "{International Journal of Mathematics in Operational Research ({IJMOR})}" }
+@string { ijista = "{International Journal of Intelligent Systems Technologies and Applications ({IJISTA})}" }
+@string { ijoc = "{{INFORMS} Journal on Computing}" }
+@string { ijoamt = "{International Journal of Advanced Manufacturing Technology ({JAMT})}" }
+@string { ijpla = "{International Journal of Programming Languages and Applications ({IJPLA})}" }
+@string { itois = "{{IEICE} Transactions on Information and Systems}" }
+@string { itor = "{International Transactions on Operational Research ({ITOR})}" }
+@string { joim = "{Journal of Intelligent Manufacturing}" }
+@string { jos = "{Journal of Scheduling}" }
+@string { joasr = "{Journal of Asian Scientific Research}" }
+@string { jsea = "{Journal of Software Engineering and Applications ({JSEA})}" }
+@string { itevc = "{{IEEE} Transactions on Evolutionary Computation ({TEVC})}" }
+@string { mans = "{Management Science}" }
+@string { ojoc = "{{ORSA} Journal on Computing}" }
+@string { or = "{Operations Research}" }
+@string { ors = "{{O}perations-{R}esearch-{S}pektrum ({OR} Spectrum)}" }
+@string { rics = "{Research in Computing Science}" }
+@string { wtoc = "{{WSEAS} Transactions on Computers}" }
+
+@string { lncs = "{Lecture Notes in Computer Science ({LNCS})}" }
+@string { lntcs = "{Theoretical Computer Science and General Issues book sub series ({LNTCS})}" }
+@string { pcs = "{Procedia Computer Science}" }
+
+@string { corr = "{Computing Research Repository {(CoRR)}}" }
+
+
+@article{A2010RIGAFTJSPACS,
+  author = {Tamer F. Abdelmaguid},
+  title = {Representations in Genetic Algorithm for the Job Shop Scheduling Problem: {A} Computational Study},
+  journal = jsea,
+  volume = {3},
+  number = {12},
+  month = dec,
+  year = {2010},
+  pages = {1155--1162},
+  doi = {10.4236/jsea.2010.312135},
+  url = {http://www.scirp.org/journal/paperinformation.aspx?paperid=3561}
+}
+
+
+@article{A2015ALSGAFTJSSPWIA,
+  author = {Leila Asadzadeh},
+  title = {A Local Search Genetic Algorithm for the Job Shop Scheduling Problem with Intelligent Agents},
+  journal = caie,
+  volume = {85},
+  pages = {376--383},
+  year = {2015},
+  month = jul,
+  doi = {10.1016/j.cie.2015.04.006}
+}
+
+
+@article{ABZ1988TSBPFJSS,
+  title = {The Shifting Bottleneck Procedure for Job Shop Scheduling},
+  author = {Joseph Adams and Egon Balas and Daniel Zawack},
+  year = {1988},
+  journal = mans,
+  volume = {34},
+  number = {3},
+  pages = {391--401},
+  doi = {10.1287/mnsc.34.3.391}
+}
+
+
+@article{AC1991ACSOTJSSP,
+  title = {A Computational Study of the Job-Shop Scheduling Problem},
+  author = {David Lee Applegate and William John Cook},
+  journal = ojoc,
+  volume = {3},
+  number = {2},
+  pages = {149--156},
+  month = may,
+  year = {1991},
+  doi = {10.1287/ijoc.3.2.149},
+  note = {the JSSP instances used were generated in Bonn in 1986}
+}
+
+@techreport{AF2002MSAFJSSRODRMD,
+  author = {M. Emin Aydin and Terrence Claus Fogarty},
+  title = {Modular Simulated Annealing for Job Shop Scheduling running on Distributed Resource Machine (DRM)},
+  institution = {London South Bank University, Faculty of Business, Computing and Information Management},
+  address = {London, England, UK},
+  year = {2002},
+  url = {http://www.soc.napier.ac.uk/~benp/dream/dreampaper6a.pdf}
+}
+
+
+@article{AK2018AIPAWGANBDOFTJSSP,
+  author = {Rehab F. Abdel{-}Kader},
+  title = {An Improved {PSO} Algorithm with Genetic and Neighborhood-Based Diversity Operators for the Job Shop Scheduling Problem},
+  journal = aaiaij,
+  volume = {32},
+  number = {5},
+  pages = {433--462},
+  year = {2018},
+  month = jun,
+  doi = {10.1080/08839514.2018.1481903}
+}
+
+
+@article{AKZ2016FSAHWQFSJSSP,
+  author = {Kashif Akram and Khurram Kamal and Alam Zeb},
+  title = {Fast Simulated Annealing Hybridized with Quenching for Solving Job Shop Scheduling Problem},
+  journal = asoc,
+  volume = {49},
+  pages = {510--523},
+  year = {2016},
+  month = dec,
+  doi = {10.1016/j.asoc.2016.08.037}
+}
+
+
+@article{AMCS2014UMHDICPREPDJSS,
+  author = {Jacob Meneses Angel and Marcela Rivera Mart{\'{\i}}nez and Luis Ren{\'{e}} Marcial Castillo and Lourdes Sandoval Solis},
+  title = {Un Modelo H{\'{\i}}brido de Inteligencia Computacional para Resolver el Problema de Job Shop Scheduling},
+  editor = {Edmundo Bonilla Huerta and Federico Ram{\'i}rez Cruz and Eduardo Sanchez Lucero},
+  journal = rics,
+  volume = {79(Advances in Intelligent Information Technologies)},
+  month = oct,
+  pages = {9--20},
+  year = {2014},
+  url = {http://www.rcs.cic.ipn.mx/2014_79/RCS_79_2014.pdf}
+}
+
+
+@inproceedings{ASS2014ABCAAFTJSSP,
+  title = {Artificial Bee Colony (ABC) Algorithm for the Job-Shop Scheduling Problem},
+  author = {Khedim Amaria and Mehdi Souier and Zaki Sar},
+  booktitle = {Proceedings of the 5th International Conference on Metaheuristics and Nature Inspired Computing ({META'14}), } # oct # {~27--31, 2014, {Marrakech}, {Morocco}},
+  year = {2014},
+  url = {https://meta2014.sciencesconf.org/42589/document},
+  note = {The paper reports makespan 53 for ft06, which is below the lower bound of 55 and thus is not included in our dataset.}
+}
+
+@article{AZ2015AEAGAFSTJSSP,
+  author = {Mehrdad Amirghasemi and Reza Zamani},
+  title = {An Effective Asexual Genetic Algorithm for Solving the Job Shop Scheduling Problem},
+  journal = caie,
+  volume = {83},
+  pages = {123--138},
+  year = {2015},
+  doi = {10.1016/j.cie.2015.02.011},
+  month = may
+}
+
+
+@article{B1995AGPATJSSWGA,
+  author = {Christian Bierwirth},
+  title = {A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms},
+  journal = ors,
+  month = jun,
+  year = {1995},
+  volume = {17},
+  number = {2--3},
+  pages = {87--92},
+  url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.52.7392&type=pdf},
+  doi = {10.1007/BF01719250}
+}
+
+
+@article{BB2001SOBIFTJSPBPHTA,
+  author = {Wolfgang Brinkk{\"o}tter and Peter Brucker},
+  title = {Solving Open Benchmark Instances for the Job-Shop Problem by Parallel Head-Tail Adjustments},
+  journal = jos,
+  volume = {4},
+  number = {1},
+  pages = {53--64},
+  year = {2001},
+  doi = {10.1002/1099-1425(200101/02)4:1<53::AID-JOS59>3.0.CO;2-Y}
+}
+
+
+@article{BFW2011CCPALSFJSS,
+  author = {J. Christopher Beck and T. K. Feng and Jean{-}Paul Watson},
+  title = {Combining Constraint Programming and Local Search for Job-Shop Scheduling},
+  journal = ijoc,
+  volume = {23},
+  number = {1},
+  pages = {1--14},
+  year = {2011},
+  doi = {10.1287/ijoc.1100.0388},
+  url = {http://cfwebprod.sandia.gov/cfdocs/CompResearch/docs/ists-sgmpcs.pdf}
+}
+
+
+@techreport{BV1994GLSWSBFJSS,
+  author = {Egon Balas and Alkis Vazacopoulos},
+  title = {Guided Local Search with Shifting Bottleneck for Job Shop Scheduling},
+  type = {Management Science Research Report},
+  number = {MSSRâ€“609},
+  institution = {Graduate School of Industrial Administration {(GSIA)}, Carnegie Mellon University},
+  year = {1994},
+  month = oct,
+  address = {Pittsburgh, {PA}, {USA}},
+  note = {revised } # nov # {~1995}
+}
+
+
+@article{BV1998GLSWSBFJSS,
+  author = {Egon Balas and Alkis Vazacopoulos},
+  title = {Guided Local Search with Shifting Bottleneck for Job Shop Scheduling},
+  journal = mans,
+  year = {1998},
+  month = feb,
+  volume = {44},
+  number = {2},
+  pages = {262--275},
+  doi = {10.1287/mnsc.44.2.262},
+  note = {reports 307 as makespan for orb07, probably a typo, as the lower bound is 397}
+}
+
+
+@misc{C2003PCOR2FTTETLOETP,
+  author = {Jo{\~a}o Paulo Caldeira},
+  title = {Private Communication of Result 2869 for ta62 to {{\'E}ric D. Taillard}, listed on {{\'E}ric Taillard's Page}},
+  url = {http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/jobshop.dir/best_lb_up.txt},
+  year = {2003},
+  month = jan
+}
+
+
+@article{CCCRZDARLAOS2019HMGMPAWCCFTJSSP,
+  title = {Hybrid Micro Genetic Multi-Population Algorithm With Collective Communication for the Job Shop Scheduling Problem},
+  author = {Marco Antonio Cruz{-}Ch{\'a}vez and Mart{\'i} H. {Cruz Rosales} and Jos{\'e} Crisp{\'i}n Zavala{-}D{\'i}az and Jos{\'e} Alberto Hern{\'a}ndez Aguilar and Abelardo Rodr{\i}guez{-}Le{\'o} and Juan Carlos Prince Avelino and Martha Elena Luna Orziz and Oscar H. Salinas},
+  journal = ia,
+  volume = {7},
+  pages = {82358--82376},
+  month = jun,
+  year = {2019},
+  doi = {10.1109/ACCESS.2019.2924218},
+  url = {http://ieeexplore.ieee.org/document/8743353}
+}
+
+
+@article{CP1989AAFSTJSP,
+  author = {Jacques Carlier and {\'E}ric Pinson},
+  title = {An Algorithm for Solving the Job-Shop Problem},
+  journal = mans,
+  volume = {35},
+  number = {2},
+  pages = {164--176},
+  year = {1989},
+  doi = {10.1287/mnsc.35.2.164},
+  note = {jstor: 2631909}
+}
+
+
+@article{CP1990APUOJPSFSTJSP,
+  author = {Jacques Carlier and {\'E}ric Pinson},
+  title = {A Practical Use of Jackson's Preemptive Schedule for Solving the Job Shop Problem},
+  journal = aor,
+  volume = {26},
+  number = {1--4},
+  pages = {269--287},
+  year = {1990},
+  month = dec
+}
+
+
+@article{CPL2016AHEATSTJSSP,
+  author = {T. C. E. Cheng and Bo Peng and Zhipeng L{\"{u}}},
+  title = {A Hybrid Evolutionary Algorithm to Solve the Job Shop Scheduling Problem},
+  journal = aor,
+  volume = {242},
+  number = {2},
+  pages = {223--237},
+  year = {2016},
+  month = jul,
+  doi = {10.1007/s10479-013-1332-5},
+  note = {The paper reports 555 as average makespan of HEA for ft20, which is an obvious typo because the other columns have 1165, which is the lower bound.}
+}
+
+
+@techreport{DMU1996BFSSP,
+  author = {Ebru Demirkol and Sanjay V. Mehta and Reha Uzsoy},
+  title = {Benchmarking for Shop Scheduling Problems},
+  type = {Research Memorandum},
+  number = {96-4},
+  institution = {School of Industrial Engineering, Purdue University},
+  address = {West Lafayette, {IN}, {USA}},
+  year = {1996}
+}
+
+
+@article{DMU1998BFSSP,
+  title = {Benchmarks for Shop Scheduling Problems},
+  author = {Ebru Demirkol and Sanjay V. Mehta and Reha Uzsoy},
+  journal = ejor,
+  volume = {109},
+  number = {1},
+  month = aug,
+  year = {1998},
+  pages = {137--141},
+  doi = {10.1016/S0377-2217(97)00019-2}
+}
+
+
+@article{DPNP2018PBAFOMIJSSP,
+  author = {Thi{-}Kien Dao and Tien{-}Szu Pan and Trong{-}The Nguyen and Jeng{-}Shyang Pan},
+  title = {Parallel Bat Algorithm for Optimizing Makespan in Job Shop Scheduling Problems},
+  journal = joim,
+  volume = {29},
+  number = {2},
+  pages = {451--462},
+  year = {2018},
+  month = feb,
+  doi = {10.1007/s10845-015-1121-x}
+}
+
+
+@inproceedings{GTK1994SJSSPBGA,
+  author = {Mitsuo Gen and Yasuhiro Tsujimura and Erika Kubota},
+  title = {Solving Job-Shop Scheduling Problems by Genetic Algorithm},
+  booktitle = {Humans, Information and Technology: Proceedings of the 1994 {IEEE} International Conference on Systems, Man and Cybernetics, } # oct # {~2--5, 1994, {San Antonio}, {TX}, {USA}},
+  volume = {2},
+  publisher = {{IEEE}},
+  year = {1994},
+  isbn = {0-7803-2129-4},
+  doi = {10.1109/ICSMC.1994.400072},
+  url = {http://read.pudn.com/downloads151/doc/658565/00400072.pdf}
+}
+
+
+@techreport{FGB2013AACOAFJSSP,
+  author = {Edson Fl{\'{o}}rez and Wilfredo G{\'{o}}mez and Lola Bautista},
+  title = {An Ant Colony Optimization Algorithm for Job Shop Scheduling Problem},
+  type = corr,
+  number = {abs/1309.5110},
+  year = {2013},
+  institution = {arXiv},
+  url = {https://arxiv.org/pdf/1309.5110.pdf},
+}
+
+
+@incollection{FT1963PLCOLJSSR,
+  author = {Henry Fisher and Gerald L. Thompson},
+  title = {Probabilistic Learning Combinations of Local Job-Shop Scheduling Rules},
+  booktitle = {Industrial Scheduling},
+  editor = {John F. Muth and Gerald L. Thompson},
+  publisher = {Prentice-Hall},
+  address = {Englewood Cliffs, {NJ}, {USA}},
+  year = {1963},
+  chapter = {3.2},
+  pages = {225--251}
+}
+
+
+@article{FTM1971AIEAFTMSP,
+  author = {Michael Florian and P. Trepant and Graham McMahon},
+  title = {An Implicit Enumeration Algorithm for the Machine Sequencing Problem},
+  journal = mans,
+  volume = {17},
+  number = {12},
+  pages = {B-782--B-792},
+  year= {1971},
+  month = aug,
+  doi = {10.1287/mnsc.17.12.B782},
+  note = {jstor: 2629469}
+}
+
+
+@article{GL2010ETSMBRSFTJSSP,
+  author = {Anis Gharbi and Mohamed Labidi},
+  title = {Extending the Single Machine-Based Relaxation Scheme for the Job Shop Scheduling Problem},
+  journal = enidm,
+  volume = {36},
+  pages = {1057--1064},
+  year = {2010},
+  month = aug,
+  doi = {10.1016/j.endm.2010.05.134},
+  note = {this algorithm was used to solve several JSSP instances of the OR Library}
+}
+
+
+@article{GLWLW2015AHABOANNSEMFSSP,
+  author = {Liang Gao and Xinyu Li and Xiaoyu Wen and Chao Lu and Feng Wen},
+  title = {A Hybrid Algorithm based on a New Neighborhood Structure Evaluation Method for Job Shop Scheduling Problem},
+  journal = caie,
+  volume = {88},
+  pages = {417--429},
+  year = {2015},
+  month = oct,
+  doi = {10.1016/j.cie.2015.08.002}
+}
+
+
+@article{GR2014AEAGMWABRKGAFJSS,
+  author = {Jos{\'{e}} Fernando Gon{\c{c}}alves and Mauricio G. C. Resende},
+  title = {An Extended {Akers} Graphical Method with a Biased Random-Key Genetic Algorithm for Job-Shop Scheduling},
+  journal = itor,
+  volume = {21},
+  number = {2},
+  pages = {215--246},
+  year = {2014},
+  month = mar,
+  url = {http://mauricio.resende.info/doc/brkga-jss2011.pdf},
+  doi = {10.1111/itor.12044}
+}
+
+
+@techreport{GvHSGT2009,
+  author = {Joaquim A. S. Gromicho and Jelke Jeroen {van Hoorn} and Francisco {Saldanha{-}da{-}Gama} and Gerrit T. Timmer},
+  title = {Exponentially Better than Brute Force: Solving the Job-Shop Scheduling Problem Optimally by Dynamic Programming},
+  type = {Research Memorandum},
+  number = {2009-56},
+  year = {2009},
+  institution = {Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam},
+  address = {Amsterdam, The Netherlands},
+  url = {http://degree.ubvu.vu.nl/repec/vua/wpaper/pdf/20090056.pdf},
+}
+
+
+@phdthesis{H2002PJSSP,
+  author = {Andr{\'e} Henning},
+  title = {{Praktische Job-Shop Scheduling-Probleme}},
+  school = {Friedrich-Schiller-Universit{\"a}t Jena},
+  institution = {Friedrich-Schiller-Universit{\"a}t Jena},
+  address = {Jena, Germany},
+  year = {2002},
+  month = aug,
+  url = {http://www.db-thueringen.de/servlets/DocumentServlet?id=873},
+  note = {alternate url: https://nbn-resolving.org/urn:nbn:de:gbv:27-20060809-115700-4}
+}
+
+
+@article{HRSCVGBTVMR2019AHSAFJSSP,
+  title = {A Hybrid Simulated Annealing for Job Shop Scheduling Problem},
+  author = {Leonor Hern{\'a}ndez{-}Ram{\'i}rez and Juan Frausto Solis and Guadalupe Castilla-Valdez and Juan Javier Gonz{\'a}lez{-}Barbosa and David Ter{\'a}n{-}Villanueva and Mar{\'i}a Lucila Morales{-}Rodr{\'i}guez},
+  journal = ijcopi,
+  volume = {10},
+  number = {1},
+  year = {2019},
+  month = jan # {--} # apr,
+  note = {published 2018-08-10},
+  pages = {6--15},
+  url = {http://ijcopi.org/index.php/ojs/article/view/111}
+}
+
+
+@article{HY2020ROAJSSPBODDD,
+  author = {Bao{-}An Han and Jian{-}Jun Yang},
+  title = {Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double {DQN}},
+  journal = ia,
+  volume = {8},
+  pages = {186474--186495},
+  year = {2020},
+  doi = {10.1109/ACCESS.2020.3029868},
+}
+
+
+@article{JM1999DJSSPPAF,
+  author = {Anant Singh Jain and Sheik Meeran},
+  title = {Deterministic Job-Shop Scheduling: {P}ast, Present and Future},
+  journal = ejor,
+  volume = {113},
+  number = {2},
+  pages = {390--434},
+  year = {1999},
+  doi = {10.1016/S0377-2217(98)00113-1},
+  month = mar
+}
+
+
+@article{JPDS2014CAODRIGAFJSSP,
+  title = {Comparative Study of Different Representations in Genetic Algorithms for Job Shop Scheduling Problem},
+  author = {Vedavyasrao Jorapur and V. S. Puranik and A. S. Deshpande and M. R. Sharma},
+  journal = jsea,
+  year = {2014},
+  month = jun,
+  volume = {7},
+  number = {7},
+  pages = {571--580},
+  doi = {10.4236/jsea.2014.77053},
+  url = {http://www.scirp.org/journal/paperinformation.aspx?paperid=46670}
+}
+
+
+@article{JZ2018AOGWOFSCPJSAFJSSC,
+  author = {Tianhua Jiang and Chao Zhang},
+  title = {Application of Grey Wolf Optimization for Solving Combinatorial Problems: {J}ob Shop and Flexible Job Shop Scheduling Cases},
+  journal = ia,
+  volume = {6},
+  pages = {26231--26240},
+  year = {2018},
+  month = may,
+  url = {http://ieeexplore.ieee.org/document/8355479},
+  doi = {10.1109/ACCESS.2018.2833552}
+}
+
+
+@article{K1999SNROSAATTJSSP,
+  author = {Michael Kolonko},
+  title = {Some New Results on Simulated Annealing Applied to the Job Shop Scheduling Problem},
+  journal = ejor,
+  volume = {113},
+  number = {1},
+  pages = {123--136},
+  year = {1999},
+  month = feb,
+  doi = {10.1016/S0377-2217(97)00420-7}
+}
+
+
+@article{K2015ANHIMGAFJSSP,
+  author = {Mohamed Kurdi},
+  title = {A New Hybrid Island Model Genetic Algorithm for Job Shop Scheduling Problem},
+  journal = caie,
+  volume = {88},
+  pages = {273--283},
+  year = {2015},
+  month = oct,
+  doi = {10.1016/j.cie.2015.07.015}
+}
+
+
+@article{KNFH2010SOJSSPBSE,
+  author = {Miyuki Koshimura and Hidetomo Nabeshima and Hiroshi Fujita and Ryuzo Hasegawa},
+  title = {Solving Open Job-Shop Scheduling Problems by {SAT} Encoding},
+  journal = itois,
+  volume = {E93.D},
+  number = {8},
+  pages = {2316--2318},
+  year = {2010},
+  doi = {10.1587/transinf.E93.D.2316},
+}
+
+
+@inproceedings{KV2014ISAOAFJSS,
+  author = {Ketki Kulkarni and Jayendran Venkateswaran},
+  title = {Iterative Simulation and Optimization Approach for Job Shop Scheduling},
+  booktitle = {Proceedings of the 2014 Winter Simulation Conference, } # dec # {~7--10, 2014, {Savannah}, {GA}, {USA}},
+  editor = {Stephen J. Buckley and John A. Miller},
+  publisher = {{IEEE/ACM}},
+  pages = {1620--1631},
+  year = {2014},
+  doi = {10.1109/WSC.2014.7020013},
+  url = {https://www.anylogic.com/upload/iblock/5aa/5aa2987b839049668eeef8a21c811e6b.pdf}
+}
+
+
+@phdthesis{L1998RCPSAEIOHSTS,
+  author = {Stephen R. Lawrence},
+  year = {1984},
+  title = {Resource Constrained Project Scheduling: An Experimental Investigation of Heuristic Scheduling Techniques (Supplement)},
+  school = {Graduate School of Industrial Administration ({GSIA}), Carnegie-Mellon University},
+  institution = {Graduate School of Industrial Administration ({GSIA}), Carnegie-Mellon University},
+  address = {Pittsburgh, {PA}, {USA}}
+}
+
+
+@inproceedings{LWF2017AITLBOATSJSSP,
+  author = {Linna Li and Wei Weng and Shigeru Fujimura},
+  title = {An Improved Teaching-Learning-based Optimization Algorithm to Solve Job Shop Scheduling Problems},
+  booktitle = {16th {IEEE/ACIS} International Conference on Computer and Information Science {(ICIS'17)}, } # may # {~24--26, 2017, {Wuhan}, {China}},
+  pages = {797--801},
+  year = {2017},
+  doi = {10.1109/ICIS.2017.7960101},
+  editor = {Guobin Zhu and Shaowen Yao and Xiaohui Cui and Simon Xu},
+  publisher = {{IEEE} Computer Society},
+  isbn = {978-1-5090-5507-4}
+}
+
+@article{LYL2020HWOAEWLFADEFJSSP,
+  author = {Min Liu and Xifan Yao and Yongxiang Li},
+  title = {Hybrid Whale Optimization Algorithm Enhanced with L{\'{e}}vy Flight and Differential Evolution for Job Shop Scheduling Problems},
+  journal = asoc,
+  volume = {87},
+  pages = {105954},
+  year = {2020},
+  month = feb,
+  doi = {10.1016/j.asoc.2019.105954},
+  note = {Originally, the paper had two typos in the results. It reports an average result (918.4) for WSO-LFDE on la20, which is worse than the worst result (902) it reports. We therefore ignore the worst reported result for that algorithm on that instance, since it was probably accidentally copy-pasted from the best result. On instance la23, the lower bound is 1032 but the result 1023 is reported, which is clearly an accidental typo. These typos are currently fixed in an erratum process.}
+}
+
+@phdthesis{M1996ATOATCOSFTJSSP,
+  author = {Paul Douglas Martin},
+  title = {A Time-Oriented Approach to Computing Optimal Schedules for the Job-Shop Scheduling Problem},
+  school = {School of Operations Research and Industrial Engineering, Cornell University},
+  institution = {School of Operations Research and Industrial Engineering, Cornell University},
+  address = {Ithaca, {NY}, {USA}},
+  year = {1996},
+  note = {oclc: 64683112}
+}
+
+
+@misc{M2012JSSUAIS,
+  title = {Bachelor's Thesis: Job Shop Scheduling using Artificial Immune System},
+  author = {Deepak Kumar Mahapatra},
+  year = {2012},
+  month = oct,
+  publisher = {Department of Mechanical Engineering, National Institute of Technology},
+  address = {Rourkela, Odisha, India},
+  url = {http://pdfs.semanticscholar.org/a350/070a2612d046d11feb33e64d1ab58cd8870d.pdf},
+  note = {guided by Prof. S. S. Mahapatra}
+}
+
+
+@article{MF1975OSWRTADDTMML,
+  author = {Graham McMahon and Michael Florian},
+  title = {On Scheduling with Ready Times and Due Dates to Minimize Maximum Lateness},
+  journal = or,
+  volume = {23},
+  number = {3},
+  pages = {475--482},
+  year = {1975},
+  month = may # {--} # jun,
+  doi = {10.1287/opre.23.3.475},
+  note = {jstor: 169697}
+}
+
+
+@inproceedings{MHT2012APGAFTJSSP,
+  author = {Nguyen Huu Mui and Vu Dinh Hoa and Luc Tri Tuyen},
+  title = {A Parallel Genetic Algorithm for the Job Shop Scheduling Problem},
+  booktitle = {Proceedings of the {IEEE} International Symposium on Signal Processing and Information
+Technology ({ISSPIT}'12), } # dec # {~12--15, 2012, {Ho} {Chi} {Minh} {City}, {Vietnam}},
+  pages = {19--24},
+  year = {2012},
+  publisher = {{IEEE} Computer Society},
+  isbn = {978-1-4673-5604-6},
+  doi = {10.1109/ISSPIT.2012.6621254}
+}
+
+
+@article{MM2013ACSOCOFGATSTJSSP,
+  title = {A Comparative Study of Crossover Operators for Genetic Algorithms to Solve the Job Shop Scheduling Problem},
+  author = {Jorge {Magalh{\~a}es-Mendes}},
+  journal = wtoc,
+  volume = {12},
+  number = {4},
+  month = apr,
+  year = {2013},
+  pages = {164--173},
+  url = {http://www.wseas.org/multimedia/journals/computers/2013/5705-156.pdf}
+}
+
+
+@article{MNKW2012HGAGFJSSPAISA,
+  author = {Shahid Maqsood and Sahar Noor and Mohammed Khurshid Khan and Alastair Wood},
+  title = {Hybrid Genetic Algorithm {(GA)} for Job Shop Scheduling Problems and its Sensitivity Analysis},
+  journal = ijista,
+  volume = {11},
+  number = {1/2},
+  pages = {49--62},
+  year = {2012},
+  doi = {10.1504/IJISTA.2012.046543}
+}
+
+
+@incollection{MTSV2018FIAFJSS,
+  author = {Joss Miller{-}Todd and Kathleen Steinh{\"{o}}fel and Patrick Veenstra},
+  title = {Firefly-Inspired Algorithm for Job Shop Scheduling},
+  booktitle = {Adventures Between Lower Bounds and Higher Altitudes -- Essays Dedicated to Juraj Hromkovi{\v{c}} on the Occasion of His 60th Birthday},
+  pages = {423--433},
+  year = {2018},
+  doi = {10.1007/978-3-319-98355-4\_24},
+  editor = {Hans{-}Joachim B{\"{o}}ckenhauer and Dennis Komm and Walter Unger},
+  series = lncs,
+  volume = {11011},
+  publisher = {Springer},
+  isbn = {978-3-319-98354-7}
+}
+
+
+@article{N2015SJSSPUAACO,
+  author = {Habibeh Nazif},
+  title = {Solving Job Shop Scheduling Problem Using an Ant Colony Algorithm},
+  journal = joasr,
+  year = {2015},
+  volume = {5},
+  number = {5},
+  pages = {261--268},
+  doi = {10.18488/journal.2/2015.5.5/2.5.261.268},
+  url = {http://www.aessweb.com/pdf-files/jasr-2015-5(5)-261-268.pdf}
+}
+
+
+@article{NA2012AHBFAFSJSSP,
+  author= {S. Narendhar and T. Amudha},
+  title = {A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems},
+  journal = ijpla,
+  volume = {2},
+  number = {4},
+  pages = {1--11},
+  month = oct,
+  year = {2012},
+  doi = {10.5121/ijpla.2012.2401},
+  url = {https://arxiv.org/pdf/1211.4971.pdf},
+  note = {Also available via Computing Research Repository {(CoRR)} abs/1211.4971 at arXiv:1211.4971v1 [cs.NE]}
+}
+
+
+@article{NS1996AFTSAFTJSP,
+  title = {A Fast Taboo Search Algorithm for the Job Shop Problem},
+  author = {Eugeniusz Nowicki and Czes{\l}aw Smutnicki},
+  journal = mans,
+  year = {1996},
+  month = jun,
+  volume = {42},
+  number = {6},
+  pages = {783--938},
+  doi = {10.1287/mnsc.42.6.797},
+  url = {http://pacciarelli.inf.uniroma3.it/CORSI/MSP/NowickiSmutnicki96.pdf},
+  note = {jstor: 2634595}
+}
+
+
+@article{NS2005AATSAFTJSP,
+  title = {An Advanced Taboo Search Algorithm for the Job Shop Problem},
+  author = {Eugeniusz Nowicki and Czes{\l}aw Smutnicki},
+  journal = jos,
+  year = {2005},
+  month = apr,
+  volume = {8},
+  number = {2},
+  pages = {145--159},
+  doi = {10.1007/s10951-005-6364-5}
+}
+
+
+@article{NZJT2013ACSORIGPTED,
+  author = {Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan},
+  title = {A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem},
+  journal = itevc,
+  month = oct,
+  volume = {17},
+  number = {5},
+  pages = {621--639},
+  year = {2013},
+  doi = {10.1109/TEVC.2012.2227326}
+}
+
+
+@inproceedings{ODP2010STJSPWARKGAWIP,
+  title = {Solving the Job Shop Problem with a Random Keys Genetic Algorithm with Instance Parameters},
+  author = {Jos{\'e} Ant{\'o}nio Oliveira and Lu{\'i}s Dias and Guilherme Pereira},
+  booktitle = {Proceedings of the 2nd International Conference on Engineering Optimization {(EngOpt2010)}, } # sep # {~6--9, 2010, {Lisbon}, {Portugal}},
+  year = {2010},
+  isbn = {978-989-96264-3-0},
+  publisher = {Associa{\c{c}}{\~{a}}o Portuguesa de Mec{\^{a}}nica Te{\'o}rica, Aplicada e Computacional ({APMTAC})},
+  address = {Lisbon, Portugal},
+  editor = {H{\'e}lder Rodrigues and Jos{\'e} Herskovits and Crist{\'o}v{\~{a}}o Mota Soares and Jos{\'e} Miranda Guedes and Jo{\~{a}}o Folgado and Aur{\'e}lio Ara{\'u}jo and Filipa Moleiro and Jayachandran P. Kuzhichalil and Jos{\'e} Aguilar Madeira and Zuzana Dimitrovov{\'a}},
+  chapter = {1512},
+  url = {http://www1.dem.ist.utl.pt/engopt2010/Book_and_CD/Papers_CD_Final_Version/pdf/08/01512-01.pdf}
+}
+
+
+@article{OV2004LSGAFTJSSP,
+  author= {Beatrice M. Ombuki and Mario Ventresca},
+  title = {Local Search Genetic Algorithms for the Job Shop Scheduling Problem},
+  journal = ai,
+  volume = {21},
+  number = {1},
+  pages = {99--109},
+  year = {2004},
+  month = jul,
+  doi = {10.1023/B:APIN.0000027769.48098.91}
+}
+
+
+@article{P2014VNSAATJSSP,
+  author = {Pisut Pongchairerks},
+  title = {Variable Neighbourhood Search Algorithms Applied to Job-Shop Scheduling Problems},
+  journal = ijmor,
+  volume = {6},
+  number = {6},
+  pages = {752--774},
+  year = {2014},
+  doi = {10.1504/IJMOR.2014.065421}
+}
+
+
+@article{P2019ATLMAFTJSSP,
+  title = {A Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem},
+  author = {Pisut Pongchairerks},
+  journal = compl,
+  volume = {2019},
+  number = {8683472},
+  year = {2019},
+  pages = {1--11},
+  doi = {10.1155/2019/8683472},
+  url = {http://www.hindawi.com/journals/complexity/2019/8683472/}
+}
+
+
+@article{PLC2015ATSPRATSTJSSP,
+  author = {Bo Peng and Zhipeng L{\"u} and T. C. E. Cheng},
+  title = {A Tabu Search/Path Relinking Algorithm to Solve the Job Shop Scheduling Problem},
+  journal = caor,
+  volume = {53},
+  pages = {154--164},
+  year = {2015},
+  month = jan,
+  doi = {10.1016/j.cor.2014.08.006},
+  url = {http://arxiv.org/abs/1402.5613},
+  note = {A } # feb # { 2014 preprint is available as arXiv:1402.5613v1 [cs.DS]},
+}
+
+
+@article{PM2000ATSMGBSBFTJSSP,
+  author = {Ferdinando Pezzella and Emanuela Merelli},
+  title = {A Tabu Search Method Guided by Shifting Bottleneck for the Job Shop Scheduling Problem},
+  journal = ejor,
+  volume = {120},
+  number = {2},
+  pages = {297--310},
+  year = {2000},
+  month = jan,
+  doi = {10.1016/S0377-2217(99)00158-7},
+  url = {https://www2.cs.sfu.ca/CourseCentral/827/havens/papers/topic\%2310(JobShop)/Tabu\%20With\%20Shifting.pdf}
+}
+
+
+@article{PPH2012AONNGAFMSITJSS,
+  author = {Elena P{\'{e}}rez and Marta Posada and Francisco Herrera},
+  title = {Analysis of New Niching Genetic Algorithms for Finding Multiple Solutions in the Job Shop Scheduling},
+  journal = joim,
+  volume = {23},
+  number = {3},
+  pages = {341--356},
+  year = {2012},
+  month = jun,
+  doi = {10.1007/s10845-010-0385-4},
+  note = {reports result 595.97 for la03, which is below the lower bound of 597 and thus not included in our data set}
+}
+
+
+@article{PSV2010SJSSPUTPOBABV,
+  author = {Panos M. Pardalos and Oleg V. Shylo and Alkis Vazacopoulos},
+  title = {Solving Job Shop Scheduling Problems Utilizing the Properties of Backbone and ``Big Valley''},
+  journal = coaa,
+  volume  = {47},
+  number = {1},
+  pages = {61--76},
+  year = {2010},
+  month = sep,
+  doi = {10.1007/s10589-008-9206-5}
+}
+
+
+@article{QL2014AABHAFSSSP,
+  author = {Xueni Qiu and Henry Y. K. Lau},
+  title = {An {AIS}-based Hybrid Algorithm for Static Job Shop Scheduling Problem},
+  journal = joim,
+  volume = {25},
+  number = {3},
+  pages = {489--503},
+  year = {2014},
+  month = jun,
+  doi = {10.1007/s10845-012-0701-2}
+}
+
+
+@inproceedings{RNK2012AKMBMPCATSJSS,
+  author = {Mohammad R. {Raeesi N.} and Ziad Kobti},
+  title = {A Knowledge-Migration-Based Multi-Population Cultural Algorithm to Solve Job Shop Scheduling},
+  booktitle = {Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference ({FLAIRS'12}), } # may # {~23--25, 2012, {Marco Island}, {FL}, {USA}},
+  year = {2012},
+  url = {http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS12/paper/view/4378/4768},
+  editor = {G. Michael Youngblood and Philip M. McCarthy},
+  publisher = {{AAAI} Press},
+  isbn = {978-1-57735-558-8}
+}
+
+
+@misc{S2019JSSPH,
+  title = {Job Shop Scheduling (Personal Homepage)},
+  author = {Oleg V. Shylo},
+  address = {Knoxville, TN, USA},
+  url = {http://optimizizer.com/jobshop.php},
+  month = aug,
+  year = {2019}
+}
+
+
+@misc{S200RLOETP,
+  author = {R. Schilham},
+  title = {Results listed on {{\'E}ric Taillard's Page}},
+  year = {2000},
+  url = {http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html},
+  note = {see also http://jobshop.jjvh.nl/}
+}
+
+
+@article{SB1999JSSWBS,
+  author = {{\.{I}}hsan Sabuncuo{\u{g}}lu and Murat Bayiz},
+  title = {Job Shop Scheduling with Beam Search},
+  journal = ejor,
+  volume = {118},
+  number = {2},
+  pages = {390--412},
+  year = {1999},
+  month = oct,
+  doi = {10.1016/S0377-2217(98)00319-1},
+  url = {http://yoksis.bilkent.edu.tr/doi_getpdf/articles/10.1016-S0377-2217(98)00319-1.pdf}
+}
+
+
+@inproceedings{SIS1997NESFSJSPBGA,
+  title = {New Encoding Scheme for Solving Job Shop Problems by Genetic Algorithm},
+  author = {Guoyong Shi and Hitoshi Iima and Nobuo Sannomiya},
+  booktitle = {Proceedings of the 35th {IEEE} Conference on Decision and Control ({CDC}'96), } # dec # {~11--13, 1996, {Kobe}, {Japan}},
+  publisher = {{IEEE}},
+  volume = {4},
+  pages = {4395--4400},
+  year = {1997},
+  isbn = {0-7803-3590-2},
+  doi = {10.1109/CDC.1996.577484}
+}
+
+
+@inproceedings{SK2000EDCFJSSPCCWJ,
+  author = {Jun Sakuma and Shigenobu Kobayashi},
+  title = {Extrapolation-Directed Crossover for Job-Shop Scheduling Problems: Complementary Combination with {JOX}},
+  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference ({GECCO}'00), } # jul # {~8--12, 2000, {Las Vegas}, {NV}, {USA}},
+  pages = {973--980},
+  year = {2000},
+  editor = {L. Darrell Whitley and David E. Goldberg and Erick Cant{\'{u}}{-}Paz and Lee Spector and Ian C. Parmee and Hans{-}Georg Beyer},
+  publisher = {Morgan Kaufmann},
+  isbn = {1-55860-708-0}
+}
+
+
+@article{SMM2018PABCPFJSSP,
+  title = {Parallel Artificial Bee Colony ({PABC}) for Job Shop Scheduling Problems},
+  author = {Sudip Kumar Sahana and Indrajit Mukherjee and Prabhat Kumar Mahanti},
+  journal = aiss,
+  volume = {10},
+  number = {3},
+  month = oct,
+  year = {2018},
+  pages = {1--11},
+  url = {http://www.globalcis.org/aiss/ppl/AISS3877PPL.pdf},
+  note = {reports 661 as result for abz9 which is below the lower bound 678 and thus not included in our data set}
+}
+
+
+@techreport{SS2018BBOVBCACSOJSS,
+  author = {Oleg V. Shylo and Hesam Shams},
+  title = {Boosting Binary Optimization via Binary Classification: A Case Study of Job Shop Scheduling},
+  type = {cs.AI/math.OC},
+  number = {abs/1808.10813},
+  year = {2018},
+  month = aug,
+  institution = {arXiv},
+  url = {https://arxiv.org/pdf/1808.10813},
+  note = {Many results are available in the GitHub repository https://github.com/quasiquasar/gta-jobshop-data. We just use a subset (namely, samples after 3, 5, 30, and 60 minutes, and the end results) to compute statistics. The paper reports some new bks for which the creating runs are not contained in the GitHub repository, verified via email with the authors, as well as bound 6196 for both dmu74 and dmu75. Other results have been published on Prof. Shylo's website http://optimizizer.com/DMU.php for the same paper (including dmu17)}
+}
+
+
+@article{SSS2018BFABCAFJSSP,
+  author = {Nirmala Sharma and Harish Sharma and Ajay Sharma},
+  title = {Beer Froth Artificial Bee Colony Algorithm for Job-Shop Scheduling Problem},
+  journal = asoc,
+  volume = {68},
+  pages = {507--524},
+  year = {2018},
+  doi = {10.1016/j.asoc.2018.04.001}
+}
+
+
+@article{SWV1992NSSFSPWATJSS,
+  title = {New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling},
+  author = {Robert H. Storer and S. David Wu and Renzo Vaccari},
+  doi = {10.1287/mnsc.38.10.1495},
+  year = {1992},
+  journal = mans,
+  volume = {38},
+  pages = {1495--1509},
+  number = {10}
+}
+
+
+@article{T199BFBSP,
+  author = {{\'E}ric D. Taillard},
+  title = {Benchmarks for Basic Scheduling Problems},
+  journal = ejor,
+  volume = {64},
+  number = {2},
+  month = jan,
+  year = {1993},
+  pages = {278--285},
+  doi = {10.1016/0377-2217(93)90182-M}
+}
+
+
+@misc{V1996ATJEBOL,
+  author = {Rob J. M. Vaessens},
+  title = {Addition to {John Edward Beasley's} {OR} Library},
+  year = {1996},
+  url = {http://people.brunel.ac.uk/~mastjjb/jeb/orlib/files/jobshop1.txt},
+  note = {see also http://jobshop.jjvh.nl/}
+}
+
+
+@misc{V1995RLOETP,
+  author = {Rob J. M. Vaessens},
+  title = {Results listed on {{\'E}ric Taillard's Page}},
+  year = {1995},
+  url = {http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html},
+  note = {see also http://jobshop.jjvh.nl/}
+}
+
+
+@article{VAL1996JSSBLS,
+  author = {Rob J. M. Vaessens and Emile H. L. Aarts and Jan Karel Lenstra},
+  title = {Job Shop Scheduling by Local Search},
+  journal = ijoc,
+  volume = {8},
+  number = {3},
+  pages = {302--317},
+  year = {1996},
+  month = summer,
+  doi = {10.1287/ijoc.8.3.302}
+}
+
+
+@misc{vH2015JSIAS,
+  author = {Jelke Jeroen {van Hoorn}},
+  title = {Job Shop Instances and Solutions},
+  url = {http://jobshop.jjvh.nl},
+  year = {2015}
+}
+
+
+@phdthesis{vH2016DPFRASOSOD,
+  title = {Dynamic Programming for Routing and Scheduling: Optimizing Sequences of Decisions},
+  author = {Jelke Jeroen {van Hoorn}},
+  year = {2016},
+  month = jun,
+  school = {Vrije Universiteit Amsterdam},
+  institution = {Vrije Universiteit Amsterdam},
+  address = {Amsterdam, The Netherlands},
+  url = {http://jobshop.jjvh.nl/dissertation},
+  isbn = {978-94-6332-008-5}
+}
+
+
+@inproceedings{VLS2015FDSFCBS,
+  author = {Petr Vil{\'i}m and Philippe Laborie and Paul Shaw},
+  title = {Failure-Directed Search for Constraint-Based Scheduling},
+  booktitle = {International Conference Integration of {AI} and {OR} Techniques in Constraint Programming: Proceedings of 12th International Conference on {AI} and {OR} Techniques in Constriant Programming for Combinatorial Optimization Problems {(CPAIOR'2015)}, } # may # {~18-22, 2015, {Barcelona, Spain}},
+  series = lncs # { and } # lntcs,
+  volume = 9075,
+  pages = {437--453},
+  year = {2015},
+  editor = {Laurent Michel},
+  publisher = {Springer},
+  address = {Cham, Switzerland},
+  doi = {10.1007/978-3-319-18008-3_30},
+  isbn = {978-3-319-18007-6}
+}
+
+
+@misc{VLS2015FDSFCBSDER,
+  author = {Petr Vil{\'i}m and Philippe Laborie and Paul Shaw},
+  title = {Failure-Directed Search for Constraint-Based Scheduling -- Detailed Experimental Results},
+  url = {http://vilim.eu/petr/cpaior2015-results.pdf},
+  year = {2015},
+  month = feb,
+  note = {The detailed experimental results of the paper ``Failure-Directed Search for Constraint-Based Scheduling'' by the same authors, in International Conference Integration of {AI} and {OR} Techniques in Constraint Programming: Proceedings of 12th International Conference on {AI} and {OR} Techniques in Constriant Programming for Combinatorial Optimization Problems {(CPAIOR'2015)}, } # may # {~18-22, 2015, {Barcelona, Spain}, pages~437--453, doi:10.1007/978-3-319-18008-3\_30.}
+}
+
+
+@misc{W2019JRDAIOTJSSP,
+  title = {jsspInstancesAndResults: Results, Data, and Instances of the Job Shop Scheduling Problem},
+  author = {Thomas Weise},
+  publisher = {Institute of Applied Optimization, Hefei University},
+  address = {Hefei, Anhui, China},
+  year = {2019--2020},
+  url = {https://github.com/thomasWeise/jsspInstancesAndResults},
+  note = {A GitHub repository with the common benchmark instances for the Job Shop Scheduling Problem as well as results from the literature, both in form of CSV files as well as R program code to access them.}
+}
+
+
+@article{WCL2016AAMPGAFJSSP,
+  title = {An Adaptive Multi-Population Genetic Algorithm for Job-Shop Scheduling Problem},
+  author = {Lei Wang and Jing{-}Cao Cai and Ming Li},
+  journal = aim,
+  month = jun,
+  year = {2016},
+  volume = {4},
+  number = {2},
+  pages = {142--149},
+  doi = {10.1007/s40436-016-0140-y}
+}
+
+
+@article{WD2014AHBBOAFJSSP,
+  author = {Xiaohua Wang and Haibin Duan},
+  title = {A Hybrid Biogeography-based Optimization Algorithm for Job Shop Scheduling Problem},
+  journal = caie,
+  volume = {73},
+  pages = {96--114},
+  year = {2014},
+  month = jul,
+  doi = {10.1016/j.cie.2014.04.006},
+  url = {http://hbduan.buaa.edu.cn/papers/2014CAIE_Wang_Duan.pdf}
+}
+
+
+@article{WGK2008ANNJSS,
+  title = {A Neural Network Job-Shop Scheduler},
+  author = {Gary R. Weckman and Chandrasekhar V. Ganduri and David A. Koonce},
+  journal = joim,
+  year = {2008},
+  month = apr,
+  volume = {19},
+  issue = {2},
+  pages = {191--201},
+  doi = {10.1007/s10845-008-0073-9}
+}
+
+
+@inproceedings{WTC2018AHPSAFJSSP,
+  author = {Shao{-}Juan Wang and Chun{-}Wei Tsai and Ming{-}Chao Chiang},
+  title = {A High Performance Search Algorithm for Job-Shop Scheduling Problem},
+  booktitle = {The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks ({EUSPN'18}) / The 8th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare ({ICTH'18}) / Affiliated Workshops, } # nov # {~5--8, 2018, {Leuven}, {Belgium}},
+  pages = {119--126},
+  year = {2018},
+  doi = {10.1016/j.procs.2018.10.157},
+  editor = {Elhadi M. Shakshuki and Ansar{-}Ul{-}Haque Yasar},
+  series = pcs,
+  volume = {141},
+  publisher = {Elsevier}
+}
+
+
+@inproceedings{YN1992AGAATLSJSI,
+  author = {Takeshi Yamada and Ryohei Nakano},
+  title = {A Genetic Algorithm Applicable to Large-Scale Job-Shop Instances},
+  booktitle = {Proceedings of Parallel Problem Solving from Nature 2 ({PPSN II}), } # sep # {~28--30, 1992, {Brussels}, {Belgium}},
+  editor = {Reinhard M{\"{a}}nner and Bernard Manderick},
+  address  = {Amsterdam, The Netherlands},
+  publisher = {Elsevier},
+  pages = {281--290},
+  year = {1992}
+}
+
+
+@inproceedings{YN1997GAFJSSP,
+  title = {Genetic Algorithms for Job-Shop Scheduling Problems},
+  author = {Takeshi Yamada and Ryohei Nakano},
+  booktitle = {Proceedings of Modern Heuristic for Decision Support, } # mar # {18--19, 1997, {London}, {England}, {UK}},
+  pages = {67--81},
+  year = {1997},
+  publisher = {{UNICOM} seminar}
+}
+
+
+@inproceedings{ZHZ2016AHFTJSSP,
+  title = {A Heuristic for the Job Shop Scheduling Problem},
+  author = {Hugo Zupan and Niko Herakovi{\v{c}} and Janez {\v{Z}}erovnik},
+  pages = {187--198},
+  url = {http://bioma.ijs.si/conference/BIOMA2016Proceedings.pdf},
+  chapter = {13},
+  booktitle = {The 7th International Conference on Bioinspired Optimization Methods and their Application {(BIOMA'16)}, } # may # {~18--20, 2016, {Bled}, {Slovenia}},
+  year = {2016},
+  editor = {Gregor Papa and Marjan Mernik},
+  publisher = {Jo{\v{z}}ef Stefan Institute},
+  address = {Ljubljana, Slovenia},
+  isbn = {978-961-264-093-4}
+}
+
+
+@article{ZLRG2008AVFTAFTJSSP,
+  title = {A Very Fast {TS/SA} Algorithm for the Job Shop Scheduling Problem},
+  author = {Chaoyong Zhang and Peigen Li and Yunqing Rao and Zailin Guan},
+  journal = caor,
+  volume = {35},
+  number = {1},
+  month = jan,
+  year = {2008},
+  pages = {282--294},
+  doi = {10.1016/j.cor.2006.02.024}
+}
+
+
+@article{ZRL2008AEHGAFTJSSP,
+  title = {An Effective Hybrid Genetic Algorithm for the Job Shop Scheduling Problem},
+  author = {Chaoyong Zhang and Yunqing Rao and Peigen Li},
+  journal = ijoamt,
+  volume = {39},
+  pages = {965--974},
+  month = nov,
+  year = {2008},
+  doi = {10.1007/s00170-007-1354-8}
+}
+
+
+@incollection{ZSRQ2008SNROTSAATTJSSP,
+  author = {Chaoyong Zhang and Xinyu Shao and Yunqing Rao and Haobo Qiu},
+  title = {Some New Results on Tabu Search Algorithm Applied to the Job-Shop Scheduling Problem},
+  month = sep,
+  year = {2008},
+  booktitle = {Tabu Search},
+  editor = {Wassim Jaziri},
+  publisher = {IntechOpen},
+  address = {{London}, {England}, {UK}},
+  isbn = {978-3-902613-34-9},
+  doi = {10.5772/5593},
+  url = {http://www.intechopen.com/books/tabu_search/some_new_results_on_tabu_search_algorithm_applied_to_the_job-shop_scheduling_problem}
+}
\ No newline at end of file
diff --git a/src/jsp_instance_utils/jsp_or_tools_solver.py b/src/jsp_instance_utils/jsp_or_tools_solver.py
index 8c07b48..f8c749f 100644
--- a/src/jsp_instance_utils/jsp_or_tools_solver.py
+++ b/src/jsp_instance_utils/jsp_or_tools_solver.py
@@ -12,6 +12,26 @@ log.setLevel(logging.INFO)
 
 
 def solve_jsp(jsp_instance: np.ndarray, plot_results: bool = True) -> (float, str, pd.DataFrame, dict):
+    """
+    :param jsp_instance: the jsp instance to solve. The jsp instance is a numpy array with the following structure:
+        np.array([
+            [
+                [0, 1, 2, 3],  # job 0
+
+                [0, 2, 1, 3]  # job 1
+            ],
+            [
+                [11, 3, 3, 12],  # task durations of job 0
+
+                [5, 16, 7, 4]  # task durations of job 1
+            ]
+        ], dtype=np.int32)
+
+    :param plot_results: if True, the results will be plotted in a gantt chart to the console.
+
+    :return: the makespan, the status of the solver, the gantt chart as pandas dataframe and additional information form
+             the solver (or-tools cp solver).
+    """
     # Create the model.
     model = cp_model.CpModel()
 
-- 
GitLab