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A repository with a data set including instances and results from literature for the Job Shop Scheduling Problem (JSSP). While the raw data is provided as text files, it is also compiled in an R package with an API around it.

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jsspInstancesAndResults: Results, Data, and Instances of the Job Shop Scheduling Problem

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Table of Contents

1. Introduction

This is a repository with a data set including instances and results from literature on the Job Shop Scheduling Problem (JSSP). Many papers on the JSSP include tables of result statistics. Here we try to provide such results from many papers in one central location, in order to make it easier to compare new works with the existing ones.

The data is presented both as text files as well as in form of an R package. This means you can either read it using your favorite programming language or by loading and processing it directly via R. We link all the data presented here directly with BibTeX entries and present the results for the different algorithms all together as well as summarize the state-of-the-art. Since the summarization and joining of data is done automatically, we can always easily add more information. If you published a paper on the JSSP, just send it to me and I will add the results.

Our goal is to have an update-able archive of the state-of-the-art results, directly linked with BibTeX entries and the functionality to generate result tables and to compare algorithms with said state-of-the-art. Currently, this project is just a preliminary version. I begun searching papers from 2019 backwards and also include papers referenced by similar repositories and some papers I found (more or less randomly), so many important works are probably still missing.

2. Installing the R Package

You can easily install the R by executing the following script in R:

if(!require("devtools")) {
  install.packages("devtools");
  library("devtools");
}
install_github("thomasWeise/jsspInstancesAndResults")

3. Provided Data

While all data is easily accessible within the R package, you can also access the raw data in form of comma-separated-values (CSV) text files as follows:

Of course, such a survey can never be complete. Thus, please expect that some data may be missing. Also, we try to provide some meta-data on the algorithms applied as well as the systems on which the experiments were run. Here, it is very easy to mis-interpret something or to make a mistake. If you have additional papers from which we can include results or wish to correct an error, contact me anytime.

4. Instance Information and Statistics

Computer-processable information about the JSSP instances can be found here as CSV and in the data frame jssp.instances in the R package.

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 – 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 ABZ 10 10 1234 AC 1234 AC 0.04 AZ
abz6 ABZ 10 10 943 AC 943 AC 0.03 AZ
abz7 ABZ 20 15 656 M 656 H 1000 H
abz8 ABZ 20 15 648 VLS 665 H 1000 H
abz9 ABZ 20 15 678 KNF 678 ZSR 3.25 AZ
dmu01 DMU1 20 15 2501 BB 2563 H 332.87 PLC
dmu02 DMU1 20 15 2651 BB 2706 H 179.24 PLC
dmu03 DMU1 20 15 2731 BB 2731 H 388.59 PLC
dmu04 DMU1 20 15 2601 BB 2669 H 96.54 PLC
dmu05 DMU1 20 15 2749 BB 2749 H 303 PLC
dmu06 DMU1 20 20 3042 vH2 3244 PSV 10000 PSV
dmu07 DMU1 20 20 2828 vH2 3046 PSV 360.58 PLC
dmu08 DMU1 20 20 3051 GL 3188 PSV 295.81 PLC
dmu09 DMU1 20 20 2956 GL 3092 H 500 H
dmu10 DMU1 20 20 2858 GL 2984 PSV 10000 PSV
dmu11 DMU1 30 15 3395 DMU 3430 PLC 1496.85 PLC
dmu12 DMU1 30 15 3481 DMU 3492 SS
dmu13 DMU1 30 15 3681 DMU 3681 GR 622.13 PLC
dmu14 DMU1 30 15 3394 DMU 3394 H 3.02 PLC
dmu15 DMU1 30 15 3343 GL 3343 H 1.77 PLC
dmu16 DMU1 30 20 3734 GL 3751 GR
dmu17 DMU1 30 20 3709 GL 3814 SS
dmu18 DMU1 30 20 3844 DMU 3844 GR 3787.4 PLC
dmu19 DMU1 30 20 3672 vH2 3765 SS
dmu20 DMU1 30 20 3604 DMU 3710 PLC 701.29 PLC
dmu21 DMU1 40 15 4380 DMU 4380 H 0.69 PLC
dmu22 DMU1 40 15 4725 DMU 4725 H 1.48 PLC
dmu23 DMU1 40 15 4668 DMU 4668 H 1.3 PLC
dmu24 DMU1 40 15 4648 DMU 4648 H 0.75 PLC
dmu25 DMU1 40 15 4164 DMU 4164 H 0.6 PLC
dmu26 DMU1 40 20 4647 DMU 4647 GR 1631.43 PLC
dmu27 DMU1 40 20 4848 DMU 4848 H 12.16 PLC
dmu28 DMU1 40 20 4692 DMU 4692 H 17.68 PLC
dmu29 DMU1 40 20 4691 DMU 4691 H 63.49 PLC
dmu30 DMU1 40 20 4732 DMU 4732 H 123 PLC
dmu31 DMU1 50 15 5640 DMU 5640 H 0.84 PLC
dmu32 DMU1 50 15 5927 DMU 5927 H 0.62 PLC
dmu33 DMU1 50 15 5728 DMU 5728 H 0.43 PLC
dmu34 DMU1 50 15 5385 DMU 5385 H 2.22 PLC
dmu35 DMU1 50 15 5635 DMU 5635 H 0.71 PLC
dmu36 DMU1 50 20 5621 DMU 5621 H 7.83 PLC
dmu37 DMU1 50 20 5851 DMU 5851 H 11.38 PLC
dmu38 DMU1 50 20 5713 DMU 5713 H 10.66 PLC
dmu39 DMU1 50 20 5747 DMU 5747 H 2.02 PLC
dmu40 DMU1 50 20 5577 DMU 5577 H 4.91 PLC
dmu41 DMU1 20 15 3007 GL 3248 PLC 417.84 PLC
dmu42 DMU1 20 15 3224 vH2 3390 PLC 448.95 PLC
dmu43 DMU1 20 15 3292 GL 3441 GR 399.33 PLC
dmu44 DMU1 20 15 3299 vH2 3475 SS
dmu45 DMU1 20 15 3039 vH2 3272 GR
dmu46 DMU1 20 20 3575 GL 4035 GR 984.86 PLC
dmu47 DMU1 20 20 3522 GL 3939 GR
dmu48 DMU1 20 20 3447 GL 3763 SS
dmu49 DMU1 20 20 3403 GL 3710 PLC 633.84 PLC
dmu50 DMU1 20 20 3496 GL 3729 PLC 609.62 PLC
dmu51 DMU1 30 15 3954 vH2 4156 SS
dmu52 DMU1 30 15 4094 vH2 4311 PLC 2232.6 PLC
dmu53 DMU1 30 15 4141 GL 4390 SS
dmu54 DMU1 30 15 4202 GL 4362 SS
dmu55 DMU1 30 15 4146 vH2 4270 SS
dmu56 DMU1 30 20 4554 GL 4941 PLC 3825.44 PLC
dmu57 DMU1 30 20 4302 GL 4663 PLC 3649.41 PLC
dmu58 DMU1 30 20 4319 GL 4708 PLC 3639.68 PLC
dmu59 DMU1 30 20 4219 vH2 4619 SS
dmu60 DMU1 30 20 4319 GL 4739 SS
dmu61 DMU1 40 15 4917 GL 5172 SS
dmu62 DMU1 40 15 5041 vH2 5251 SS
dmu63 DMU1 40 15 5111 GL 5323 SS
dmu64 DMU1 40 15 5130 DMU 5240 SS
dmu65 DMU1 40 15 5107 vH2 5190 SS
dmu66 DMU1 40 20 5397 vH2 5717 PLC 9543.86 PLC
dmu67 DMU1 40 20 5589 GL 5779 SS
dmu68 DMU1 40 20 5426 GL 5765 SS
dmu69 DMU1 40 20 5423 GL 5709 PLC 8107.63 PLC
dmu70 DMU1 40 20 5501 GL 5889 SS
dmu71 DMU1 50 15 6080 GL 6223 PLC 9835.11 PLC
dmu72 DMU1 50 15 6395 GL 6463 SS
dmu73 DMU1 50 15 6001 GL 6153 SS
dmu74 DMU1 50 15 6123 GL 6196 SS
dmu75 DMU1 50 15 6029 GL 6189 SS
dmu76 DMU1 50 20 6342 GL 6807 SS
dmu77 DMU1 50 20 6499 GL 6792 SS
dmu78 DMU1 50 20 6586 GL 6770 PLC 10346.61 PLC
dmu79 DMU1 50 20 6650 GL 6952 SS
dmu80 DMU1 50 20 6459 GL 6673 SS
ft06 FT 6 6 55 FTM 55 CP 0 AZ
ft10 FT 10 10 930 CP 930 CP 0.06 AZ
ft20 FT 20 5 1165 MF 1165 CP 0.18 PLC
la01 L 10 5 666 ABZ 666 AC 0 AZ
la02 L 10 5 655 ABZ 655 AC 0.015 AZ
la03 L 10 5 597 AC 597 AC 0.016 AZ
la04 L 10 5 590 AC 590 AC 0.015 AZ
la05 L 10 5 593 ABZ 593 AC 0 AZ
la06 L 15 5 926 ABZ 926 AC 0 AZ
la07 L 15 5 890 ABZ 890 AC 0 AZ
la08 L 15 5 863 ABZ 863 AC 0 AZ
la09 L 15 5 951 ABZ 951 AC 0 AZ
la10 L 15 5 958 ABZ 958 AC 0 AZ
la11 L 20 5 1222 ABZ 1222 AC 0 AZ
la12 L 20 5 1039 ABZ 1039 AC 0 AZ
la13 L 20 5 1150 ABZ 1150 AC 0 AZ
la14 L 20 5 1292 ABZ 1292 AC 0 AZ
la15 L 20 5 1207 ABZ 1207 AC 0.016 AZ
la16 L 10 10 945 CP1 945 AC 0.06 CCC
la17 L 10 10 784 CP1 784 AC 0.016 AZ
la18 L 10 10 848 AC 848 AC 0.015 AZ
la19 L 10 10 842 AC 842 AC 0.025 AZ
la20 L 10 10 902 AC 902 AC 0.031 AZ
la21 L 15 10 1046 VAL 1046 YN1 7.33 PLC
la22 L 15 10 927 AC 927 AC 0.109 AZ
la23 L 15 10 1032 ABZ 1032 AC 0.047 AZ
la24 L 15 10 935 AC 935 AC 0.2 AZ
la25 L 15 10 977 AC 977 AC 0.33 AZ
la26 L 20 10 1218 ABZ 1218 AC 0.078 AZ
la27 L 20 10 1235 ABZ 1235 YN1 0.95 AZ
la28 L 20 10 1216 ABZ 1216 AC 0.109 AZ
la29 L 20 10 1152 M 1152 H 1000 H
la30 L 20 10 1355 ABZ 1355 AC 0.093 AZ
la31 L 30 10 1784 ABZ 1784 AC 0 AZ
la32 L 30 10 1850 ABZ 1850 AC 0.047 AZ
la33 L 30 10 1719 ABZ 1719 AC 0.031 AZ
la34 L 30 10 1721 ABZ 1721 AC 0.156 AZ
la35 L 30 10 1888 ABZ 1888 AC 0.046 AZ
la36 L 15 15 1268 CP1 1268 AC 0.57 AZ
la37 L 15 15 1397 AC 1397 AC 0.51 AZ
la38 L 15 15 1196 VAL 1196 NS 1.25 AZ
la39 L 15 15 1233 AC 1233 AC 0.5 AZ
la40 L 15 15 1222 AC 1222 AC 384.8 PLC
orb01 AC 10 10 1059 AC 1059 AC 0.06 AZ
orb02 AC 10 10 888 AC 888 AC 0.06 AZ
orb03 AC 10 10 1005 AC 1005 AC 0.15 AZ
orb04 AC 10 10 1005 AC 1005 AC 0.1 CCC
orb05 AC 10 10 887 AC 887 AC 0.76 AZ
orb06 AC 10 10 1010 JM 1010 BV1 0.72 AZ
orb07 AC 10 10 397 JM 397 H 0.02 AZ
orb08 AC 10 10 899 JM 899 BV1 0.09 AZ
orb09 AC 10 10 934 JM 934 BV1 0.09 AZ
orb10 AC 10 10 944 JM 944 BV1 0.03 AZ
swv01 SWV 20 10 1407 M 1407 H 575.76 PLC
swv02 SWV 20 10 1475 M 1475 H 136.94 AZ
swv03 SWV 20 10 1398 BB 1398 H 613 PLC
swv04 SWV 20 10 1464 VLS 1464 VLS2 30000 VLS2
swv05 SWV 20 10 1424 M 1424 H 1000 H
swv06 SWV 20 15 1630 VLS 1671 PLC, VLS2 385.73 PLC
swv07 SWV 20 15 1513 VLS 1594 GR
swv08 SWV 20 15 1671 VLS 1752 PLC, VLS2 503 PLC
swv09 SWV 20 15 1633 VLS 1655 PLC, VLS2 521.91 PLC
swv10 SWV 20 15 1663 VLS 1743 GR 441.4 PLC
swv11 SWV 50 10 2983 V1 2983 NS2 940.68 PLC
swv12 SWV 50 10 2972 V1 2977 PLC 6097.35 PLC
swv13 SWV 50 10 3104 V1 3104 H 1000 H
swv14 SWV 50 10 2968 BV 2968 H 422.81 PLC
swv15 SWV 50 10 2885 V1 2885 PLC 6000.57 PLC
swv16 SWV 50 10 2924 SWV 2924 H 1000 H
swv17 SWV 50 10 2794 SWV 2794 H 1000 H
swv18 SWV 50 10 2852 SWV 2852 H 1000 H
swv19 SWV 50 10 2843 SWV 2843 H 1000 H
swv20 SWV 50 10 2823 SWV 2823 H 1000 H
ta01 T 15 15 1231 T 1231 H 2.93 PLC
ta02 T 15 15 1244 V 1244 NS 38.09 PLC
ta03 T 15 15 1218 BB 1218 H 43.66 PLC
ta04 T 15 15 1175 BB 1175 PM 38.72 PLC
ta05 T 15 15 1224 BB 1224 H 11.24 PLC
ta06 T 15 15 1238 BB 1238 H 178.06 PLC
ta07 T 15 15 1227 BB 1227 H 1000 H
ta08 T 15 15 1217 BB 1217 H 2.43 PLC
ta09 T 15 15 1274 BB 1274 H 18.66 PLC
ta10 T 15 15 1241 V 1241 H 42.25 PLC
ta11 T 20 15 1357 VLS 1357 BFW 186.19 PLC
ta12 T 20 15 1367 VLS 1367 H 206.06 PLC
ta13 T 20 15 1342 VLS 1342 H 161.37 PLC
ta14 T 20 15 1345 V 1345 NS 6 SS
ta15 T 20 15 1339 VLS 1339 PSV 173.45 PLC
ta16 T 20 15 1360 VLS 1360 H 63.41 PLC
ta17 T 20 15 1462 S 1462 H 1000 H
ta18 T 20 15 1377 VLS 1396 H 91.13 PLC
ta19 T 20 15 1332 VLS 1332 PSV 145.42 PLC
ta20 T 20 15 1348 VLS 1348 PSV 216.72 PLC
ta21 T 20 20 1642 VLS 1642 BFW 3600 BFW
ta22 T 20 20 1561 VLS 1600 H 228.9 PLC
ta23 T 20 20 1518 VLS 1557 H 359.79 PLC
ta24 T 20 20 1644 VLS 1644 VLS2 30000 VLS2
ta25 T 20 20 1558 VLS 1595 NS2 416.08 PLC
ta26 T 20 20 1591 VLS 1643 GR 30000 VLS2
ta27 T 20 20 1652 VLS 1680 H 254.74 PLC
ta28 T 20 20 1603 VLS 1603 PSV 1514 SS
ta29 T 20 20 1573 VLS 1625 H 93.53 PLC
ta30 T 20 20 1519 VLS 1584 H 388.66 PLC
ta31 T 30 15 1764 T 1764 H 6 SS
ta32 T 30 15 1774 T 1784 S2
ta33 T 30 15 1788 VLS 1791 PSV 457.55 PLC
ta34 T 30 15 1828 T 1829 H 315.71 PLC
ta35 T 30 15 2007 V 2007 PM 0.56 PLC
ta36 T 30 15 1819 V 1819 H 15 SS
ta37 T 30 15 1771 T 1771 GR 652.24 PLC
ta38 T 30 15 1673 T 1673 H 45 SS
ta39 T 30 15 1795 V 1795 H 6 SS
ta40 T 30 15 1651 VLS 1669 GR 30000 VLS2
ta41 T 30 20 1906 VLS 2005 VLS2 30000 VLS2
ta42 T 30 20 1884 VLS 1937 GR 30000 VLS2
ta43 T 30 20 1809 V 1846 PLC 1726.78 PLC
ta44 T 30 20 1948 VLS 1979 VLS2 30000 VLS2
ta45 T 30 20 1997 V 2000 H 1057.79 PLC
ta46 T 30 20 1957 VLS 2004 GR 30000 VLS2
ta47 T 30 20 1807 VLS 1889 PLC, VLS2 1030.88 PLC
ta48 T 30 20 1912 V 1937 SS 3008 SS
ta49 T 30 20 1931 VLS 1961 VLS2 30000 VLS2
ta50 T 30 20 1833 VLS 1923 PLC, VLS2 1318.05 PLC
ta51 T 50 15 2760 T 2760 PM 2000 H
ta52 T 50 15 2756 T 2756 PM 2000 H
ta53 T 50 15 2717 T 2717 PM 2000 H
ta54 T 50 15 2839 T 2839 PM 2000 H
ta55 T 50 15 2679 T 2679 NS 2000 H
ta56 T 50 15 2781 T 2781 PM 2000 H
ta57 T 50 15 2943 T 2943 PM 2000 H
ta58 T 50 15 2885 T 2885 PM 2000 H
ta59 T 50 15 2655 T 2655 PM 2000 H
ta60 T 50 15 2723 T 2723 PM 2000 H
ta61 T 50 20 2868 T 2868 NS 2000 H
ta62 T 50 20 2869 V 2869 C
ta63 T 50 20 2755 T 2755 NS 2000 H
ta64 T 50 20 2702 BV 2702 NS 2000 H
ta65 T 50 20 2725 T 2725 NS 2000 H
ta66 T 50 20 2845 T 2845 NS 2000 H
ta67 T 50 20 2825 V 2825 H 2000 H
ta68 T 50 20 2784 BV 2784 NS 2000 H
ta69 T 50 20 3071 T 3071 NS 2000 H
ta70 T 50 20 2995 T 2995 NS 2000 H
ta71 T 100 20 5464 T 5464 PM 2000 H
ta72 T 100 20 5181 T 5181 PM 2000 H
ta73 T 100 20 5568 T 5568 PM 2000 H
ta74 T 100 20 5339 T 5339 PM 2000 H
ta75 T 100 20 5392 T 5392 PM 2000 H
ta76 T 100 20 5342 T 5342 PM 2000 H
ta77 T 100 20 5436 T 5436 PM 2000 H
ta78 T 100 20 5394 T 5394 PM 2000 H
ta79 T 100 20 5358 T 5358 PM 2000 H
ta80 T 100 20 5183 T 5183 NS 2000 H
yn1 YN 20 20 884 KNF 884 ZSR 169.29 PLC
yn2 YN 20 20 870 BB 904 GR 202.22 PLC
yn3 YN 20 20 859 VLS 892 NS2 344.15 PLC
yn4 YN 20 20 929 VLS 968 H 320.51 PLC

5. 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. The bibliography keys there will start with the same mnemonic as used here, but here we shortened these keys for the sake of brevity.

A
Abdelmaguid TF (2010). “Representations in Genetic Algorithm for the Job Shop Scheduling Problem: A Computational Study.” Journal of Software Engineering and Applications (JSEA), 3(12), 1155-1162. doi:10.4236/jsea.2010.312135, http://www.scirp.org/journal/paperinformation.aspx?paperid=3561. BibTeX:A2010RIGAFTJSPACS
A2
Asadzadeh L (2015). “A Local Search Genetic Algorithm for the Job Shop Scheduling Problem with Intelligent Agents.” Computers & Industrial Engineering, 85, 376-383. doi:10.1016/j.cie.2015.04.006. BibTeX:A2015ALSGAFTJSSPWIA
ABZ
Adams J, Balas E, Zawack D (1988). “The Shifting Bottleneck Procedure for Job Shop Scheduling.” Management Science, 34(3), 391-401. doi:10.1287/mnsc.34.3.391. BibTeX:ABZ1988TSBPFJSS
AC
Applegate DL, Cook WJ (1991). “A Computational Study of the Job-Shop Scheduling Problem.” ORSA Journal on Computing, 3(2), 149-156. doi:10.1287/ijoc.3.2.149, the JSSP instances used were generated in Bonn in 1986. BibTeX:AC1991ACSOTJSSP
AF
Aydin ME, Fogarty TC (2002). “Modular Simulated Annealing for Job Shop Scheduling running on Distributed Resource Machine (DRM).” London South Bank University, Faculty of Business, Computing and Information Management, London, England, UK. http://www.soc.napier.ac.uk/~benp/dream/dreampaper6a.pdf. BibTeX:AF2002MSAFJSSRODRMD
AK
Abdel-Kader RF (2018). “An Improved PSO Algorithm with Genetic and Neighborhood-Based Diversity Operators for the Job Shop Scheduling Problem.” Applied Artificial Intelligence - An International Journal, 32(5), 433-462. doi:10.1080/08839514.2018.1481903. BibTeX:AK2018AIPAWGANBDOFTJSSP
AKZ
Akram K, Kamal K, Zeb A (2016). “Fast Simulated Annealing Hybridized with Quenching for Solving Job Shop Scheduling Problem.” Applied Soft Computing Journal (ASOC), 49, 510-523. doi:10.1016/j.asoc.2016.08.037. BibTeX:AKZ2016FSAHWQFSJSSP
AMC
Angel JM, Martínez MR, Castillo LRM, Solis LS (2014). “Un Modelo Híbrido de Inteligencia Computacional para Resolver el Problema de Job Shop Scheduling.” Research in Computing Science, 79(Advances in Intelligent Information Technologies), 9-20. http://www.rcs.cic.ipn.mx/2014_79/RCS_79_2014.pdf. BibTeX:AMCS2014UMHDICPREPDJSS
ASS
Amaria K, Souier M, Sar Z (2014). “Artificial Bee Colony (ABC) Algorithm for the Job-Shop Scheduling Problem.” 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., https://meta2014.sciencesconf.org/42589/document. BibTeX:ASS2014ABCAAFTJSSP
AZ
Amirghasemi M, Zamani R (2015). “An Effective Asexual Genetic Algorithm for Solving the Job Shop Scheduling Problem.” Computers & Industrial Engineering, 83, 123-138. doi:10.1016/j.cie.2015.02.011. BibTeX:AZ2015AEAGAFSTJSSP
B
Bierwirth C (1995). “A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms.” Operations-Research-Spektrum (OR Spectrum), 17(2-3), 87-92. doi:10.1007/BF01719250, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.52.7392&type=pdf. BibTeX:B1995AGPATJSSWGA
BB
Brinkkötter W, Brucker P (2001). “Solving Open Benchmark Instances for the Job-Shop Problem by Parallel Head-Tail Adjustments.” Journal of Scheduling, 4(1), 53-64. doi:10.1002/1099-1425(200101/02)4:1<53::AID-JOS59>3.0.CO;2-Y4:1<53::AID-JOS59>3.0.CO;2-Y). BibTeX:BB2001SOBIFTJSPBPHTA
BFW
Beck JC, Feng TK, Watson J (2011). “Combining Constraint Programming and Local Search for Job-Shop Scheduling.” INFORMS Journal on Computing, 23(1), 1-14. doi:10.1287/ijoc.1100.0388, http://cfwebprod.sandia.gov/cfdocs/CompResearch/docs/ists-sgmpcs.pdf. BibTeX:BFW2011CCPALSFJSS
BV
Balas E, Vazacopoulos A (1994). “Guided Local Search with Shifting Bottleneck for Job Shop Scheduling.” Management Science Research Report MSSR–609, Graduate School of Industrial Administration (GSIA), Carnegie Mellon University, Pittsburgh, PA, USA. revised November 1995. BibTeX:BV1994GLSWSBFJSS
BV1
Balas E, Vazacopoulos A (1998). “Guided Local Search with Shifting Bottleneck for Job Shop Scheduling.” Management Science, 44(2), 262-275. doi:10.1287/mnsc.44.2.262, reports 307 as makespan for orb07, probably a typo, as the lower bound is 397. BibTeX:BV1998GLSWSBFJSS
C
Caldeira JP (2003). “Private Communication of Result 2869 for ta62 to Éric D. Taillard, listed on Éric Taillard's Page.” http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/jobshop.dir/best_lb_up.txt. BibTeX:C2003PCOR2FTTETLOETP
CCC
Cruz-Chávez MA, Cruz Rosales MH, Zavala-Díaz JC, Aguilar JAH, Rodrıguez-Leó A, Avelino JCP, Orziz MEL, Salinas OH (2019). “Hybrid Micro Genetic Multi-Population Algorithm With Collective Communication for the Job Shop Scheduling Problem.” IEEE Access, 7, 82358-82376. doi:10.1109/ACCESS.2019.2924218, http://ieeexplore.ieee.org/document/8743353. BibTeX:CCCRZDARLAOS2019HMGMPAWCCFTJSSP
CP
Carlier J, Pinson É (1989). “An Algorithm for Solving the Job-Shop Problem.” Management Science, 35(2), 164-176. doi:10.1287/mnsc.35.2.164, jstor: 2631909. BibTeX:CP1989AAFSTJSP
CP1
Carlier J, Pinson É (1990). “A Practical Use of Jackson's Preemptive Schedule for Solving the Job Shop Problem.” Annals of Operations Research, 26(1-4), 269-287. BibTeX:CP1990APUOJPSFSTJSP
CPL
Cheng TCE, Peng B, Lü Z (2016). “A Hybrid Evolutionary Algorithm to Solve the Job Shop Scheduling Problem.” Annals of Operations Research, 242(2), 223-237. doi:10.1007/s10479-013-1332-5, 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:CPL2016AHEATSTJSSP
DMU
Demirkol E, Mehta SV, Uzsoy R (1996). “Benchmarking for Shop Scheduling Problems.” Research Memorandum 96-4, School of Industrial Engineering, Purdue University, West Lafayette, IN, USA. BibTeX:DMU1996BFSSP
DMU1
Demirkol E, Mehta SV, Uzsoy R (1998). “Benchmarks for Shop Scheduling Problems.” European Journal of Operational Research (EJOR), 109(1), 137-141. doi:10.1016/S0377-2217(97)00019-200019-2). BibTeX:DMU1998BFSSP
DPN
Dao T, Pan T, Nguyen T, Pan J (2018). “Parallel Bat Algorithm for Optimizing Makespan in Job Shop Scheduling Problems.” Journal of Intelligent Manufacturing, 29(2), 451-462. doi:10.1007/s10845-015-1121-x. BibTeX:DPNP2018PBAFOMIJSSP
FGB
Flórez E, Gómez W, Bautista L (2013). “An Ant Colony Optimization Algorithm for Job Shop Scheduling Problem.” Computing Research Repository (CoRR) abs/1309.5110, arXiv. https://arxiv.org/pdf/1309.5110.pdf. BibTeX:FGB2013AACOAFJSSP
FT
Fisher H, Thompson GL (1963). “Probabilistic Learning Combinations of Local Job-Shop Scheduling Rules.” In Muth JF, Thompson GL (eds.), Industrial Scheduling, 225-251. Prentice-Hall, Englewood Cliffs, NJ, USA. BibTeX:FT1963PLCOLJSSR
FTM
Florian M, Trepant P, McMahon G (1971). “An Implicit Enumeration Algorithm for the Machine Sequencing Problem.” Management Science, 17(12), B-782-B-792. doi:10.1287/mnsc.17.12.B782, jstor: 2629469. BibTeX:FTM1971AIEAFTMSP
GL
Gharbi A, Labidi M (2010). “Extending the Single Machine-Based Relaxation Scheme for the Job Shop Scheduling Problem.” Electronic Notes in Discrete Mathematics, 36, 1057-1064. doi:10.1016/j.endm.2010.05.134, this algorithm was used to solve several JSSP instances of the OR Library. BibTeX:GL2010ETSMBRSFTJSSP
GLW
Gao L, Li X, Wen X, Lu C, Wen F (2015). “A Hybrid Algorithm based on a New Neighborhood Structure Evaluation Method for Job Shop Scheduling Problem.” Computers & Industrial Engineering, 88, 417-429. doi:10.1016/j.cie.2015.08.002. BibTeX:GLWLW2015AHABOANNSEMFSSP
GR
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Henning A (2002). Praktische Job-Shop Scheduling-Probleme. Ph.D. thesis, Friedrich-Schiller-Universität Jena, Jena, Germany. alternate url: https://nbn-resolving.org/urn:nbn:de:gbv:27-20060809-115700-4, http://www.db-thueringen.de/servlets/DocumentServlet?id=873. BibTeX:H2002PJSSP
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Han B, Yang J (2020). “Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN.” IEEE Access, 8, 186474-186495. doi:10.1109/ACCESS.2020.3029868. BibTeX:HY2020ROAJSSPBODDD
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Jorapur V, Puranik VS, Deshpande AS, Sharma MR (2014). “Comparative Study of Different Representations in Genetic Algorithms for Job Shop Scheduling Problem.” Journal of Software Engineering and Applications (JSEA), 7(7), 571-580. doi:10.4236/jsea.2014.77053, http://www.scirp.org/journal/paperinformation.aspx?paperid=46670. BibTeX:JPDS2014CAODRIGAFJSSP
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Jiang T, Zhang C (2018). “Application of Grey Wolf Optimization for Solving Combinatorial Problems: Job Shop and Flexible Job Shop Scheduling Cases.” IEEE Access, 6, 26231-26240. doi:10.1109/ACCESS.2018.2833552, http://ieeexplore.ieee.org/document/8355479. BibTeX:JZ2018AOGWOFSCPJSAFJSSC
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LYL
Liu M, Yao X, Li Y (2020). “Hybrid Whale Optimization Algorithm Enhanced with Lévy Flight and Differential Evolution for Job Shop Scheduling Problems.” Applied Soft Computing Journal (ASOC), 87, 105954. doi:10.1016/j.asoc.2019.105954, 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:LYL2020HWOAEWLFADEFJSSP
M
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:M1996ATOATCOSFTJSSP
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MM
Magalhães-Mendes J (2013). “A Comparative Study of Crossover Operators for Genetic Algorithms to Solve the Job Shop Scheduling Problem.” WSEAS Transactions on Computers, 12(4), 164-173. http://www.wseas.org/multimedia/journals/computers/2013/5705-156.pdf. BibTeX:MM2013ACSOCOFGATSTJSSP
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Maqsood S, Noor S, Khan MK, Wood A (2012). “Hybrid Genetic Algorithm (GA) for Job Shop Scheduling Problems and its Sensitivity Analysis.” International Journal of Intelligent Systems Technologies and Applications (IJISTA), 11(1/2), 49-62. doi:10.1504/IJISTA.2012.046543. BibTeX:MNKW2012HGAGFJSSPAISA
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Nazif H (2015). “Solving Job Shop Scheduling Problem Using an Ant Colony Algorithm.” Journal of Asian Scientific Research, 5(5), 261-268. doi:10.18488/journal.2/2015.5.5/2.5.261.268, http://www.aessweb.com/pdf-files/jasr-2015-5(5)-261-268.pdf. BibTeX:N2015SJSSPUAACO
NA
Narendhar S, Amudha T (2012). “A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems.” International Journal of Programming Languages and Applications (IJPLA), 2(4), 1-11. doi:10.5121/ijpla.2012.2401, Also available via Computing Research Repository (CoRR) abs/1211.4971 at arXiv:1211.4971v1 [cs.NE], https://arxiv.org/pdf/1211.4971.pdf. BibTeX:NA2012AHBFAFSJSSP
NS
Nowicki E, Smutnicki C (1996). “A Fast Taboo Search Algorithm for the Job Shop Problem.” Management Science, 42(6), 783-938. doi:10.1287/mnsc.42.6.797, jstor: 2634595, http://pacciarelli.inf.uniroma3.it/CORSI/MSP/NowickiSmutnicki96.pdf. BibTeX:NS1996AFTSAFTJSP
NS2
Nowicki E, Smutnicki C (2005). “An Advanced Taboo Search Algorithm for the Job Shop Problem.” Journal of Scheduling, 8(2), 145-159. doi:10.1007/s10951-005-6364-5. BibTeX:NS2005AATSAFTJSP
NZJ
Nguyen S, Zhang M, Johnston M, Tan KC (2013). “A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem.” IEEE Transactions on Evolutionary Computation (TEVC), 17(5), 621-639. doi:10.1109/TEVC.2012.2227326. BibTeX:NZJT2013ACSORIGPTED
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Oliveira JA, Dias L, Pereira G (2010). “Solving the Job Shop Problem with a Random Keys Genetic Algorithm with Instance Parameters.” In Rodrigues H, Herskovits J, Soares CM, Guedes JM, Folgado J, Araújo A, Moleiro F, Kuzhichalil JP, Madeira JA, Dimitrovová Z (eds.), Proceedings of the 2nd International Conference on Engineering Optimization (EngOpt2010), September 6-9, 2010, Lisbon, Portugal. ISBN 978-989-96264-3-0, http://www1.dem.ist.utl.pt/engopt2010/Book_and_CD/Papers_CD_Final_Version/pdf/08/01512-01.pdf. BibTeX:ODP2010STJSPWARKGAWIP
OV
Ombuki BM, Ventresca M (2004). “Local Search Genetic Algorithms for the Job Shop Scheduling Problem.” Applied Intelligence - The International Journal of Research on Intelligent Systems for Real Life Complex Problems, 21(1), 99-109. doi:10.1023/B:APIN.0000027769.48098.91. BibTeX:OV2004LSGAFTJSSP
P
Pongchairerks P (2014). “Variable Neighbourhood Search Algorithms Applied to Job-Shop Scheduling Problems.” International Journal of Mathematics in Operational Research (IJMOR), 6(6), 752-774. doi:10.1504/IJMOR.2014.065421. BibTeX:P2014VNSAATJSSP
P2
Pongchairerks P (2019). “A Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem.” Complexity, 2019(8683472), 1-11. doi:10.1155/2019/8683472, http://www.hindawi.com/journals/complexity/2019/8683472/. BibTeX:P2019ATLMAFTJSSP
PLC
Peng B, Lü Z, Cheng TCE (2015). “A Tabu Search/Path Relinking Algorithm to Solve the Job Shop Scheduling Problem.” Computers & Operations Research, 53, 154-164. doi:10.1016/j.cor.2014.08.006, A February 2014 preprint is available as arXiv:1402.5613v1 [cs.DS], http://arxiv.org/abs/1402.5613. BibTeX:PLC2015ATSPRATSTJSSP
PM
Pezzella F, Merelli E (2000). “A Tabu Search Method Guided by Shifting Bottleneck for the Job Shop Scheduling Problem.” European Journal of Operational Research (EJOR), 120(2), 297-310. doi:10.1016/S0377-2217(99)00158-700158-7), https://www2.cs.sfu.ca/CourseCentral/827/havens/papers/topic%2310(JobShop)/Tabu%20With%20Shifting.pdf. BibTeX:PM2000ATSMGBSBFTJSSP
PPH
Pérez E, Posada M, Herrera F (2012). “Analysis of New Niching Genetic Algorithms for Finding Multiple Solutions in the Job Shop Scheduling.” Journal of Intelligent Manufacturing, 23(3), 341-356. doi:10.1007/s10845-010-0385-4, reports result 595.97 for la03, which is below the lower bound of 597 and thus not included in our data set. BibTeX:PPH2012AONNGAFMSITJSS
PSV
Pardalos PM, Shylo OV, Vazacopoulos A (2010). “Solving Job Shop Scheduling Problems Utilizing the Properties of Backbone and "Big Valley".” Computational Optimization and Applications, 47(1), 61-76. doi:10.1007/s10589-008-9206-5. BibTeX:PSV2010SJSSPUTPOBABV
QL
Qiu X, Lau HYK (2014). “An AIS-based Hybrid Algorithm for Static Job Shop Scheduling Problem.” Journal of Intelligent Manufacturing, 25(3), 489-503. doi:10.1007/s10845-012-0701-2. BibTeX:QL2014AABHAFSSSP
RNK
Raeesi N. MR, Kobti Z (2012). “A Knowledge-Migration-Based Multi-Population Cultural Algorithm to Solve Job Shop Scheduling.” 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, http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS12/paper/view/4378/4768. BibTeX:RNK2012AKMBMPCATSJSS
S
Schilham R (2000). “Results listed on Éric Taillard's Page.” see also http://jobshop.jjvh.nl/, http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html. BibTeX:S200RLOETP
S2
Shylo OV (2019). “Job Shop Scheduling (Personal Homepage).” http://optimizizer.com/jobshop.php. BibTeX:S2019JSSPH
SB
Sabuncuoğlu İ, Bayiz M (1999). “Job Shop Scheduling with Beam Search.” European Journal of Operational Research (EJOR), 118(2), 390-412. doi:10.1016/S0377-2217(98)00319-100319-1), http://yoksis.bilkent.edu.tr/doi_getpdf/articles/10.1016-S0377-2217(98)00319-1.pdf. BibTeX:SB1999JSSWBS
SIS
Shi G, Iima H, Sannomiya N (1997). “New Encoding Scheme for Solving Job Shop Problems by Genetic Algorithm.” 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:10.1109/CDC.1996.577484. BibTeX:SIS1997NESFSJSPBGA
SK
Sakuma J, Kobayashi S (2000). “Extrapolation-Directed Crossover for Job-Shop Scheduling Problems: Complementary Combination with JOX.” In Whitley LD, Goldberg DE, Cantú-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:SK2000EDCFJSSPCCWJ
SMM
Sahana SK, Mukherjee I, Mahanti PK (2018). “Parallel Artificial Bee Colony (PABC) for Job Shop Scheduling Problems.” 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, http://www.globalcis.org/aiss/ppl/AISS3877PPL.pdf. BibTeX:SMM2018PABCPFJSSP
SS
Shylo OV, Shams H (2018). “Boosting Binary Optimization via Binary Classification: A Case Study of Job Shop Scheduling.” 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), https://arxiv.org/pdf/1808.10813. BibTeX:SS2018BBOVBCACSOJSS
SSS
Sharma N, Sharma H, Sharma A (2018). “Beer Froth Artificial Bee Colony Algorithm for Job-Shop Scheduling Problem.” Applied Soft Computing Journal (ASOC), 68, 507-524. doi:10.1016/j.asoc.2018.04.001. BibTeX:SSS2018BFABCAFJSSP
SWV
Storer RH, Wu SD, Vaccari R (1992). “New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling.” Management Science, 38(10), 1495-1509. doi:10.1287/mnsc.38.10.1495. BibTeX:SWV1992NSSFSPWATJSS
T
Taillard ÉD (1993). “Benchmarks for Basic Scheduling Problems.” European Journal of Operational Research (EJOR), 64(2), 278-285. doi:10.1016/0377-2217(93)90182-M90182-M). BibTeX:T199BFBSP
V
Vaessens RJM (1995). “Results listed on Éric Taillard's Page.” see also http://jobshop.jjvh.nl/, http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html. BibTeX:V1995RLOETP
V1
Vaessens RJM (1996). “Addition to John Edward Beasley's OR Library.” see also http://jobshop.jjvh.nl/, http://people.brunel.ac.uk/~mastjjb/jeb/orlib/files/jobshop1.txt. BibTeX:V1996ATJEBOL
VAL
Vaessens RJM, Aarts EHL, Lenstra JK (1996). “Job Shop Scheduling by Local Search.” INFORMS Journal on Computing, 8(3), 302-317. doi:10.1287/ijoc.8.3.302. BibTeX:VAL1996JSSBLS
vH
van Hoorn JJ (2015). “Job Shop Instances and Solutions.” http://jobshop.jjvh.nl. BibTeX:vH2015JSIAS
vH2
van Hoorn JJ (2016). Dynamic Programming for Routing and Scheduling: Optimizing Sequences of Decisions. Ph.D. thesis, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. http://jobshop.jjvh.nl/dissertation. BibTeX:vH2016DPFRASOSOD
VLS
Vilím P, Laborie P, Shaw P (2015). “Failure-Directed Search for Constraint-Based Scheduling.” 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:10.1007/978-3-319-18008-3_30. BibTeX:VLS2015FDSFCBS
VLS2
Vilím P, Laborie P, Shaw P (2015). “Failure-Directed Search for Constraint-Based Scheduling - Detailed Experimental Results.” 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., http://vilim.eu/petr/cpaior2015-results.pdf. BibTeX:VLS2015FDSFCBSDER
W
Weise T (2019-2020). “jsspInstancesAndResults: Results, Data, and Instances of the Job Shop Scheduling Problem.” 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., https://github.com/thomasWeise/jsspInstancesAndResults. BibTeX:W2019JRDAIOTJSSP
WCL
Wang L, Cai J, Li M (2016). “An Adaptive Multi-Population Genetic Algorithm for Job-Shop Scheduling Problem.” Advances in Manufacturing, 4(2), 142-149. doi:10.1007/s40436-016-0140-y. BibTeX:WCL2016AAMPGAFJSSP
WD
Wang X, Duan H (2014). “A Hybrid Biogeography-based Optimization Algorithm for Job Shop Scheduling Problem.” Computers & Industrial Engineering, 73, 96-114. doi:10.1016/j.cie.2014.04.006, http://hbduan.buaa.edu.cn/papers/2014CAIE_Wang_Duan.pdf. BibTeX:WD2014AHBBOAFJSSP
WGK
Weckman GR, Ganduri CV, Koonce DA (2008). “A Neural Network Job-Shop Scheduler.” Journal of Intelligent Manufacturing, 19, 191-201. doi:10.1007/s10845-008-0073-9. BibTeX:WGK2008ANNJSS
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Wang S, Tsai C, Chiang M (2018). “A High Performance Search Algorithm for Job-Shop Scheduling Problem.” 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:10.1016/j.procs.2018.10.157. BibTeX:WTC2018AHPSAFJSSP
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Yamada T, Nakano R (1992). “A Genetic Algorithm Applicable to Large-Scale Job-Shop Instances.” In Männer R, Manderick B (eds.), Proceedings of Parallel Problem Solving from Nature 2 (PPSN II), September 28-30, 1992, Brussels, Belgium, 281-290. BibTeX:YN1992AGAATLSJSI
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Yamada T, Nakano R (1997). “Genetic Algorithms for Job-Shop Scheduling Problems.” In Proceedings of Modern Heuristic for Decision Support, March18-19, 1997, London, England, UK, 67-81. BibTeX:YN1997GAFJSSP
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Zupan H, Herakovič N, Žerovnik J (2016). “A Heuristic for the Job Shop Scheduling Problem.” 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, http://bioma.ijs.si/conference/BIOMA2016Proceedings.pdf. BibTeX:ZHZ2016AHFTJSSP
ZLR
Zhang C, Li P, Rao Y, Guan Z (2008). “A Very Fast TS/SA Algorithm for the Job Shop Scheduling Problem.” Computers & Operations Research, 35(1), 282-294. doi:10.1016/j.cor.2006.02.024. BibTeX:ZLRG2008AVFTAFTJSSP
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Zhang C, Rao Y, Li P (2008). “An Effective Hybrid Genetic Algorithm for the Job Shop Scheduling Problem.” International Journal of Advanced Manufacturing Technology (JAMT), 39, 965-974. doi:10.1007/s00170-007-1354-8. BibTeX:ZRL2008AEHGAFTJSSP
ZSR
Zhang C, Shao X, Rao Y, Qiu H (2008). “Some New Results on Tabu Search Algorithm Applied to the Job-Shop Scheduling Problem.” In Jaziri W (ed.), Tabu Search. IntechOpen, London, England, UK. ISBN 978-3-902613-34-9, doi:10.5772/5593, http://www.intechopen.com/books/tabu_search/some_new_results_on_tabu_search_algorithm_applied_to_the_job-shop_scheduling_problem. BibTeX:ZSRQ2008SNROTSAATTJSSP

6. Cite this Package as follows

Weise T (2019-2020). “jsspInstancesAndResults: Results, Data, and Instances of the Job Shop Scheduling Problem.” 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., https://github.com/thomasWeise/jsspInstancesAndResults.

@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.}
}

7. Repository Structure

In the folder data-raw, we provide the R scripts used to generate the data frames in the package, the complete data CSV files, and this README. The idea is that we maintain a central BibTeX file (reflected in data frame jssp.bibliography) and a list of algorithms as well as a list of basic instance information. For each algorithm, a CSV text file is included with the results of that algorithm in the corresponding publication. The scripts then merge all this information into one central CSV file with all the results and provide the data as data frame jssp.results. From these results, we then automatically update the instance information and obtain an instance information file with best-known solutions, reflected in data frame jssp.instances. This is then used together with the bibliography to build our README.md. This structure allows us to easily update the repository with new results, while providing the full table of all data from literature. Finally, we generate a single, OR-Library compatible file with all of the JSSP instances in this study, such that you can easily load them and do your own experiments. The data from this file is provided as the list jssp.instance.data in the R package. In summary, all the data is provided both as text files for processing with arbitary tools and as as data frames/lists jssp.bibliography, jssp.results, jssp.instances, and jssp.instance.data if you install this repository as R package (see above).

8. Additional Functionality in the R Package

In the package jsspInstancesAndResults, we additionally provide the functionality to transform different representations for candidate solutions into Gantt charts (which are directly checked and evaluated in the process). Existing Gantt charts can also be evaluated, i.e., we can check whether the Gantt chart is correct and compute its makespan. If the plotteR package is installed, then the Gantt charts can directly be plotted.

data.oo <- c( 2L,  8L, 10L, 12L,  7L, 20L, 18L,   1L,  6L, 11L,
             17L,  9L, 28L,  5L, 30L, 19L, 21L,  38L, 22L,  3L,
             40L, 15L, 48L, 13L, 31L, 27L, 37L,  16L, 58L, 41L,
             50L, 32L, 25L, 23L, 47L,  4L, 68L,  60L, 29L, 39L,
             51L, 26L, 42L, 35L, 33L, 49L, 57L,  70L, 36L, 45L,
             61L, 14L, 55L, 67L, 78L, 43L, 71L,  53L, 52L, 80L,
             59L, 63L, 24L, 81L, 46L, 90L, 62L, 100L, 73L, 65L,
             88L, 56L, 72L, 77L, 34L, 87L, 44L,  98L, 69L, 66L,
             75L, 79L, 54L, 83L, 89L, 82L, 76L,  64L, 91L, 74L,
             99L, 93L, 92L, 86L, 84L, 85L, 96L,  95L, 97L, 94L);
result <- jssp.oo.to.gantt(data.oo, "orb07");
print(result$makespan);
# [1] 397
plotteR::plot.gantt(result$gantt);

Image resulting from the above code sample.

9. Other Useful Resources

Many of the data in this package are gathered from different sources in the internet, which were our starting point to explore and add results from quite a few publications.

9.1. Overviews of Results

Besides our repository, the following sources in the web provide useful information about the state-of-the-art on the JSSP:

9.2. Benchmark Instances

10. License

Any content in this repository which originates from other sources is licensed under the licensing conditions of the respective owners. This includes the results of works published in literature as well as the benchmarking instances. Any of the above which permits me setting a license and any content contributed by myself is under the GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007.

11. Contact

If you have any questions or suggestions, please contact Prof. Dr. Thomas Weise of the Institute of Applied Optimization at Hefei University in Hefei, Anhui, China via email to tweise@hfuu.edu.cn and tweise@gmx.de.

About

A repository with a data set including instances and results from literature for the Job Shop Scheduling Problem (JSSP). While the raw data is provided as text files, it is also compiled in an R package with an API around it.

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r csv research scheduling publications literature results papers metaheuristics job-shop-scheduling jssp

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