An effective architecture for learning and evolving flexible job-shop schedules

European Journal of Operational Research

Volumn 179, Issue 2, 2007, Pages 316-333

  Ho, Nhu Binh ^a   Tay, Joc Cing ^a   Lai, Edmund M K ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Composite Dispatching Rules; Flexible Job Shop problems; Genetic Algorithms; Scheduling

Indexed keywords

EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; INTEGRATION; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; PROBLEM SOLVING;

COMPOSITE DISPATCHING RULES; COMPUTATIONAL TIME; FLEXIBLE JOB-SHOP PROBLEMS; LEARNABLE GENETIC ARCHITECTURE (LEGA);

SCHEDULING;

EID: 33751418065     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2006.04.007     Document Type: Article

References (32)

1. Jain A.S., and Meeran S. Deterministic job-shop scheduling: Past, present and future. European Journal of Operation Research 113 2 (1998) 390-434
2. Carlier J., and Pinson E. An algorithm for solving the job-shop problem. Management Science 35 2 (1999) 164-176
3. Kolonko M. Some new results on simulated annealing applied to the job shop scheduling problem. European Journal of Operational Research 113 (1999) 123-136
4. Nowicki E., and Smutnicki C. A fast Taboo Search Algorithm for the job shop problem. Management Science 42 6 (1996) 797-813
5. T. Yamada, R. Nakano, A fusion of crossover and local search, in: Proceedings of the IEEE International Conference on Industrial Technology, 1996, pp. 426-430.
6. Garey M.R., Johnson D.S., and Sethi R. The complexity of flow shop and job-shop scheduling. Mathematics of Operations Research 1 2 (1996) 117-129
7. Pinedo M., and Chao X. Operations Scheduling with Applications in Manufacturing and Services (1999), McGraw-Hill, New York (Chapter 3)
8. Reynolds R.G. An introduction to Cultural Algorithms. Proceedings of the Third Annual Conference on Evolutionary Programming, River Edge, NJ (1994), World Scientific, Singapore 131-139
9. C.J. Chung, R.G. Reynolds, A testbed for solving optimization problems using cultural algorithm, in: Proceedings of the Fifth Annual Conference on Evolutionary Programming, 1996, pp. 225-236.
10. J. Branke, Memory-enhanced evolutionary algorithms for dynamic optimization problems, in: Proceedings of Congress on Evolutionary Computation, CEC99, 1999, pp. 1875-1882.
11. Louis S.J., and McDonnell J. Learning with case-injected Genetic Algorithms. IEEE Transactions on Evolutionary Computation 8 4 (2004)
12. Michalski R.S. Learnable Evolution Model: Evolution process guided by Machine Learning. Machine Learning 38 (2000) 9-40
13. K. Mesghouni, S. Hammadi, P. Borne, Evolution programs for job-shop scheduling, in: Proceedings of the IEEE International Conference on Computational Cybernetics and Simulation, vol. 1, 1997, pp. 720-725.
14. H. Chen, J. Ihlow, C. Lehmann, A genetic algorithm for flexible job-shop scheduling, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, 1999, pp. 1120-1125.
15. Kacem I., Hammadi S., and Borne P. Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man and Cybernetics 32 1 (2002) 1-13
16. Kacem I., Hammadi S., and Borne P. Pareto-optimality approach for flexible job-shop scheduling problems: Hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation 60 (2002) 245-276
17. Brandimarte P. Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research 22 (1993) 158-183
18. Pinedo M. Scheduling Theory, Algorithms, and Systems (2002), Prentice-Hall, Englewood Cliffs, NJ (Chapter 2)
19. N.B. Ho, J.C. Tay, The model of job assignments in Electronic Manufacturing Service in CEI Contract Manufacturing Company, Technical Report, 2002.
20. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning (1989), Addition-Wesley, Reading, MA
21. N.B. Ho, J.C. Tay, GENACE: An efficient cultural algorithm for solving the flexible job-shop problem, in: Proceedings of the Congress on Evolutionary Computation CEC2004, 2004, pp. 1759-1766.
22. J.C. Tay, D. Wibowo, An effective chromosome representation for evolving flexible job-shop schedules, in: Proceedings of the Genetic and Evolutionary Computation GECCO2004, 2004, pp. 210-221.
23. Oliver H., and Chandrasekharan R. Efficient dispatching rules for scheduling in a job shop. International Journal of Production Economics 48 1 (1997) 87-105
24. Panwalkar S., and Wafik I. A survey of scheduling rules. Operations Research 25 1 (1997) 45-61
25. Fortemps Ph., Ost Ch., Pirlot M., Teghem J., and Tuyttens D. Using metaheuristics for solving a production scheduling problem in a chemical firm. A case study. International Journal of Production Economics 46 (1996) 13-26
26. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning (2001), Springer, New York (Chapter 13)
27. Cheng R., Gen M., and Tsujimura Y. A tutorial survey of job-shop scheduling problems using genetic algorithms, Part I: Representation. Computers and Industrial Engineering 30 4 (1996) 983-997
28. Bierwirth Ch. A generalized permutation approach to job shop scheduling with genetic algorithms. OR Spektrum 17 (1995) 87-92
29. Varela R., Vela C.R., Puente J., and Gómez A. A knowledge-based evolutionary strategy for scheduling problems with bottlenecks. European Journal of Operational Research 145 1 (2003) 57-71
30. Wolpert D.H., and Macready W.G. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1 1 (1997) 67-82
31. Doyle J. A truth maintenance system. Artificial Intelligence 12 (1979) 231-272
32. Michalski R.S., and Kaufman K. Learning patterns in noisy data: The AQ approach. In: Paliouras G., Karkaletsis V., and Spyropoulos (Eds). Machine Learning sand Applications (2001), Springer, Berlin