jssp.instances: The Instance Meta-Data for the Common Benchmark Instances of...
In thomasWeise/jsspInstancesAndResults: A repository with a data set including instances and results from literature for the Job Shop Scheduling Problem (JSSP)
Description
Usage
Format
References
Examples
Description
Here we provide baseline data about the benchmark instances that are commonly used in research
on the Job Shop Scheduling Problem.
Data on the lower bounds has in particular been taken from van Hoorn's great website http://jobshop.jjvh.nl.
The data on the best-known solutions (inst.bks) has automatically been extracted from the per-instance results, see jssp.results.
All literature referenced, on the other hand, can be found in jssp.bibliography.
The benchmark instances themselves are provided in jssp.instance.data.
We also try to provide the fastest runtime that was reported in any of our referenced papers for finding inst.bks.
But please, please, take the corresponding inst.bks.time column 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.
Usage
1
Format
A data frame with the basic information about the JSSP instances
- inst.id
the unique id identifying the instance
- inst.ref
the id of the reference of the paper proposing the instance, which points into a row of jssp.bibliography
- inst.jobs
the number of jobs in the instance
- inst.machines
the number of machines in the instance
- inst.opt.bound.lower
the lower bound for the optimal solution quality
- inst.opt.bound.lower.ref
the id of the reference of the source where this lower bound has been published, which points into a row of jssp.bibliography
- inst.bks
the best-known solution for the instance within any paper cited in this study (or NA if none of the paper has one)
- inst.bks.ref
the reference(s) for the best-known for the instance within any paper cited in this study, only considering references from the earliest year the bks was found in this study, separated by ';' if multiple, NA if none
- inst.bks.time
the fasted time (in milliseconds) recorded within the papers cited in this study within which the best-known solution inst.bks was found, regardless of the computer used. Not all studies provide runtimes and if they do, the measurements may not be very precise. For some instances, we can therefore only not NA and when a value is given, it may be very unreliable. While I tried to be conservative here, consider this column merely as a general guide to get some impression about where we are standing, take it with a grain of salt, and do not consider it as ground truth.
- inst.bks.time.ref
the earliest publication reporting the time for discovering the best-known solution.
References
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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).
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.
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.
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.
Gonçalves JF, Resende MGC (2014). “An Extended Akers Graphical Method with a Biased Random-Key Genetic Algorithm for Job-Shop Scheduling.” International Transactions on Operational Research (ITOR), 21(2), 215-246. doi:10.1111/itor.12044, http://mauricio.resende.info/doc/brkga-jss2011.pdf.
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.
Jain AS, Meeran S (1999). “Deterministic Job-Shop Scheduling: Past, Present and Future.” European Journal of Operational Research (EJOR), 113(2), 390-434. doi:10.1016/S0377-2217(98)00113-100113-1).
Koshimura M, Nabeshima H, Fujita H, Hasegawa R (2010). “Solving Open Job-Shop Scheduling Problems by SAT Encoding.” IEICE Transactions on Information and Systems, E93.D(8), 2316-2318. doi:10.1587/transinf.E93.D.2316.
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.
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.
McMahon G, Florian M (1975). “On Scheduling with Ready Times and Due Dates to Minimize Maximum Lateness.” Operations Research, 23(3), 475-482. doi:10.1287/opre.23.3.475, jstor: 169697.
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.
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.
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.
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.
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.
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.
Shylo OV (2019). “Job Shop Scheduling (Personal Homepage).” http://optimizizer.com/jobshop.php.
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.
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.
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).
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.
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.
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.
van Hoorn JJ (2015). “Job Shop Instances and Solutions.” http://jobshop.jjvh.nl.
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.
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.
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.
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.
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.
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.
Examples
1
2
3
4
5
data(jssp.instances)
## Not run:
print(jssp.instances)
## End(Not run)
thomasWeise/jsspInstancesAndResults documentation built on Nov. 26, 2020, 10:03 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Format References Examples
Description
Here we provide baseline data about the benchmark instances that are commonly used in research
on the Job Shop Scheduling Problem.
Data on the lower bounds has in particular been taken from van Hoorn's great website http://jobshop.jjvh.nl.
The data on the best-known solutions (inst.bks) has automatically been extracted from the per-instance results, see jssp.results.
All literature referenced, on the other hand, can be found in jssp.bibliography.
The benchmark instances themselves are provided in jssp.instance.data.
We also try to provide the fastest runtime that was reported in any of our referenced papers for finding inst.bks.
But please, please, take the corresponding inst.bks.time column 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.
Usage
1 |
Format
A data frame with the basic information about the JSSP instances
- inst.id
the unique id identifying the instance
- inst.ref
the id of the reference of the paper proposing the instance, which points into a row of
jssp.bibliography- inst.jobs
the number of jobs in the instance
- inst.machines
the number of machines in the instance
- inst.opt.bound.lower
the lower bound for the optimal solution quality
- inst.opt.bound.lower.ref
the id of the reference of the source where this lower bound has been published, which points into a row of
jssp.bibliography- inst.bks
the best-known solution for the instance within any paper cited in this study (or NA if none of the paper has one)
- inst.bks.ref
the reference(s) for the best-known for the instance within any paper cited in this study, only considering references from the earliest year the bks was found in this study, separated by ';' if multiple, NA if none
- inst.bks.time
the fasted time (in milliseconds) recorded within the papers cited in this study within which the best-known solution
inst.bkswas found, regardless of the computer used. Not all studies provide runtimes and if they do, the measurements may not be very precise. For some instances, we can therefore only notNAand when a value is given, it may be very unreliable. While I tried to be conservative here, consider this column merely as a general guide to get some impression about where we are standing, take it with a grain of salt, and do not consider it as ground truth.- inst.bks.time.ref
the earliest publication reporting the time for discovering the best-known solution.
References
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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).
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.
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.
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.
Gonçalves JF, Resende MGC (2014). “An Extended Akers Graphical Method with a Biased Random-Key Genetic Algorithm for Job-Shop Scheduling.” International Transactions on Operational Research (ITOR), 21(2), 215-246. doi:10.1111/itor.12044, http://mauricio.resende.info/doc/brkga-jss2011.pdf.
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.
Jain AS, Meeran S (1999). “Deterministic Job-Shop Scheduling: Past, Present and Future.” European Journal of Operational Research (EJOR), 113(2), 390-434. doi:10.1016/S0377-2217(98)00113-100113-1).
Koshimura M, Nabeshima H, Fujita H, Hasegawa R (2010). “Solving Open Job-Shop Scheduling Problems by SAT Encoding.” IEICE Transactions on Information and Systems, E93.D(8), 2316-2318. doi:10.1587/transinf.E93.D.2316.
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.
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.
McMahon G, Florian M (1975). “On Scheduling with Ready Times and Due Dates to Minimize Maximum Lateness.” Operations Research, 23(3), 475-482. doi:10.1287/opre.23.3.475, jstor: 169697.
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.
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.
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.
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.
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.
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.
Shylo OV (2019). “Job Shop Scheduling (Personal Homepage).” http://optimizizer.com/jobshop.php.
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.
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.
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).
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.
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.
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.
van Hoorn JJ (2015). “Job Shop Instances and Solutions.” http://jobshop.jjvh.nl.
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.
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.
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.
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.
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.
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.
Examples
1 2 3 4 5 | data(jssp.instances)
## Not run:
print(jssp.instances)
## End(Not run)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.