A comparative study of crossover operators for genetic algorithms to solve the job shop scheduling problem

WSEAS Transactions on Computers

Volumn 12, Issue 4, 2013, Pages 164-173

  Magalhães Mendes, Jorge ^a  

^a INSTITUTO SUPERIOR DE ENGENHARIA DO PORTO   (Portugal)

Author keywords

Crossover Operators; Genetic Algorithms; JSSP; Operations Research; Optimization; Scheduling

Indexed keywords

COMBINATORIAL OPTIMIZATION PROBLEMS; COMPARATIVE STUDIES; CROSSOVER OPERATOR; DECISION SUPPORT SYSTEM (DSS); EXPERIMENTAL STUDIES; GLOBAL OPTIMIZATION METHOD; JOB SHOP SCHEDULING PROBLEMS; JSSP;

ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS; GLOBAL OPTIMIZATION; OPERATIONS RESEARCH; OPTIMIZATION; SCHEDULING; SCHEDULING ALGORITHMS;

GENETIC ALGORITHMS;

EID: 84879905690     PISSN: 11092750     EISSN: 22242872     Source Type: Journal    
DOI: None     Document Type: Article

References (41)

1. D. E. Goldberg. Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley, 1989.
2. J. Magalhães-Mendes, Project scheduling under multiple resources constraints using a genetic algorithm, WSEAS TRANSACTIONS on BUSINESS and ECONOMICS, Issue 11, Volume 5, November 2008, pp. 487-496.
3. I. Essafi, Y. Mati and S. D. Pérès, A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem, Computers & Operations Research, Vol. 35, Issue 8, 2008, pp. 2599-2616.
4. J. F. Gonçalves, J. M. Mendes, and M. C. G. Resende. A hybrid genetic algorithm for the job shop scheduling problem. European Journal of Operational Research, Vol. 167, 2005, pp. 77-95.
5. J. J. M. Mendes, J. F. Gonçalves and M. G. C. Resende, A random key based genetic algorithm for the resource constrained project scheduling problem. Comput. Oper. Res. 36, 1, 2009, pp. 92-109.
6. D. Y. Sha and C. Hsu, A hybrid particle swarm optimization for job shop scheduling problem. Computers & Industrial Engineering, 51, 4, 2006, pp. 791-808.
7. F. Pezzela and E. Merelli, A tabu search method guided by shifting bottleneck for the job shop scheduling problem, European Journal of Operational Research, Vol. 120, 2000, pp. 297-310.
8. C. Rego and R. Duarte, A filter-and-fan approach to the job shop scheduling problem, European Journal of Operational Research, Vol. 194, 2009, pp. 650-662.
9. E. Nowicki and C. Smutnicki, A Fast Taboo Search Algorithm for the Job-Shop Problem, Management Science, Vol. 42, No. 6, 1996, pp. 797-813.
10. J. Adams, E. Balas and Z. Zawack, The shifting bottleneck procedure for job shop scheduling, Management Science, Vol. 34, 1988, pp. 391-401.
11. C. Y. Zhang, P. Li. and Z. Guan, A very fast TS/SA algorithm for the job shop scheduling problem, Computers & Operations Research, Vol. 35, 2008, pp. 282-294.
12. H. R. Lourenço, Local optimization and the job-shop scheduling problem, European Journal of Operational Research, Vol. 83, 1995, pp. 347-364.
13. H. Fisher and G. L. Thompson, Probabilistic Learning Combinations of Local Job-Shop Scheduling Rules, in: Industrial Scheduling, J. F. Muth and G. L. Thompson (eds.), Prentice-Hall, Englewood Cliffs, NJ, 1963, pp. 225-251.
14. S. Lawrence, Resource Constrained Project Scheduling: An Experimental Investigation of Heuristic Scheduling Techniques, GSIA, Carnegie Mellon University, Pittsburgh, PA, 1984.
15. E. H. L. Aarts, PJ. M. Van Laarhoven, J. K. Lenstra and N. LJ. Ulder, A computational study of local search algorithms for job shop scheduling, ORSA Journal on Computing, 6, 1994, pp. 118-125.
16. R. M. Aiex, S. Binato and M. G. C. Resende, Parallel GRASP with Path-Relinking for Job Shop Scheduling, Parallel Computing, Vol. 29, Issue 4, 2003, pp. 393-430.
17. F. Croce, R. Tadei, and G. Volta, A Genetic Algorithm for the Job Shop Problem, Computers and Operations Research, Vol. 22(1), 1995, pp. 15-24.
18. U. Dorndorf, and E. Pesch, Evolution Based Learning in a Job Shop Environment, Computers and Operations Research, Vol. 22, 1995, pp. 25-40.
19. L. Wang, and D. Zheng, An effective hybrid optimisation strategy for job-shop scheduling problems, Computers & Operations Research, Vol. 28, 2001, pp. 585-596.
20. S. Binato, W. J. Hery, D. M. Loewenstern and M. G. C. Resende, A GRASP for Job Shop Scheduling. In: Essays and Surveys in Metaheuristics, Ribeiro, Celso C., Hansen, Pierre (Eds.), Kluwer Academic Publishers, 2002.
21. B. Giffler and G. L. Thompson, Algorithms for Solving Production Scheduling Problems, Operations Research, Vol. 8(4), 1960, pp. 487-503.
22. J. Carlier and E. Pinson, An Algorithm for Solving the Job Shop Problem. Management Science, Feb, 35(29), 1989, pp. 164-176.
23. J. Carlier and E. Pinson, A practical use of Jackson's preemptive schedule for solving the job-shop problem. Annals of Operations Research, Vol. 26, 1990, pp. 269-287.
24. P. Brucker, B. Jurisch and B. Sievers, A Branch and Bound Algorithm for Job-Shop Scheduling Problem, Discrete Applied Mathematics, Vol. 49, 1994, pp. 105-127.
25. D. P. Williamson, L. A. Hall, J. A. Hoogeveen, C. A. J. Hurkens, J. K. Lenstra, S. V. Sevastjanov and D. B. Shmoys, Short Shop Schedules, Operations Research, 45(2), 1997, pp. 288-294.
26. J. Magalhães-Mendes, Project scheduling using a competitive genetic algorithm. In Proceedings of the 8th WSEAS Conference on Simulation, Modelling and Optimization (Santander, Cantabria, Spain). J. M. de la Maza and P. L. Espí, Eds. Mathematics And Computers In Science And Engineering. WSEAS, Stevens Point, Wisconsin, 2008, pp. 39-42.
27. S. M. K. Hasan, R. Sarker and D. Cornforth, GA with Priority Rules for Solving Job-Shop Scheduling Problems. In Proceedings of the IEEE Congress on Evolutionary Computation CEC(2008), 2008, pp. 1913-1920.
28. M. J. Varnamkhasti, L. S. Lee, M. R. Bakar, and W. J. Leong, A Genetic Algorithm with Fuzzy Crossover Operator and Probability, Advances in Operations Research, vol. 2012, Article ID 956498, 16 pages, 2012. doi:10.1155/2012/956498
29. D. Applegate and W. Cook, A computational study of the job-shop scheduling problem. ORSA Journal on Computing, 3(2), 1991, pp. 149-156.
30. J. Magalhães-Mendes, A genetic algorithm for the job shop scheduling with a new local search using Monte Carlo method, In Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases (AIKED'11), Cambridge, UK, 2011, pp. 26-31.
31. E. Nowicki and C. Smutnicki, An Advanced Tabu Search Algorithm for the Job Shop Problem, Journal of Scheduling, 8, 2005, pp. 145-159.
32. P. Pardalos, O. Shylo and A. Vazacopoulos, Solving job shop scheduling problems utilizing the properties of backbone and big valley, Computational Optimization and Applications, 47(1), 2010, pp. 1-16.
33. G. Sywerda, Uniform crossover in genetic algorithms, in Proceedings of the 3rd International Conference on Genetic Algorithms, 1989, pp. 2-9.
34. K. A. De Jong and W. M. Spears, A formal analysis of the role of multi-point crossover in genetic algorithms, Annals of Mathematics and Artificial Intelligence, vol. 5, no. 1, 1992, pp. 1-26.
35. C. Y. Zhang, P. G. Li, Z. L. Guan, and Y. Q. Rao. A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem. Computers & Operations Research, 34(11), 2007, pp. 3229-3242.
36. Y.-Z. Luo, G.-J. Tang, Z. G. Wang and H. Y. Li. Optimization of perturbed and constrained fuel-optimal impulsive rendezvous using a hybrid approach. Engineering Optimization, 38(8), 2006, pp. 959-973.
37. R. R. Sharapov. Genetic Algorithms: Basic Ideas, Variants and Analysis, Vision Systems: Segmentation and Pattern Recognition, Goro Obinata and Ashish Dutta (Ed.), 2007, pp. 407-422, ISBN: 978-3-902613-05-9.
38. R. Qing-doa-er-ji and Y. Wang. A new hybrid genetic algorithm for job shop scheduling problem. Computers & Operations Research, 39(10), 2012, pp. 2291-2299.
39. H. R. Choi and B. J. Park, Genetic Algorithm for the Integration of Process Planning and Scheduling in a Job Shop, WSEAS TRANSACTIONS on INFORMATION SCIENCE & APPLICATIONS, Issue 12, Volume 3, December 2006, pp. 2498-2504.
40. H. Chiu, K. Hsieh, Y. T. Tang and W. Chien, Employing a Genetic Algorithm Based on Knowledge to Address the Job Shop Scheduling Problem, WSEAS TRANSACTIONS on COMPUTER RESEARCH, Issue 2, Volume 2, February 2007, pp. 327-333.
41. H. Chiu, K. Hsieh, Y. T. Tang and C. Y. Wang, A Novel Approach to Address the Job-Shop Scheduling Problem by using a Tabu Genetic Algorithm, WSEAS TRANSACTIONS on COMPUTER RESEARCH, Issue 2, Volume 2, February 2007, pp. 339-345.