Industrial Engineering and Management Systems

Korean Institute of Industrial Engineers (대한산업공학회)
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A Genetic Algorithm with a New Repair Process for Solving Multi-stage, Multi-machine, Multi-product Scheduling Problems

  • Pongcharoen, Pupong (Industrial Engineering Department, Faculty of Engineering Naresuan University) ;
  • Khadwilard, Aphirak (Mechanical Engineering Department, Faculty of Engineering Rajamangala University of Technology Lanna, Tak Campus) ;
  • Hicks, Christian (Business School, University of Newcastle upon Tyne)
  • Published : 2008.12.31
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Abstract

Companies that produce capital goods need to schedule the production of products that have complex product structures with components that require many operations on different machines. A feasible schedule must satisfy operation and assembly precedence constraints. It is also important to avoid deadlock situations. In this paper a Genetic Algorithm (GA) has been developed that includes a new repair process that rectifies infeasible schedules that are produced during the evolution process. The algorithm was designed to minimise the combination of earliness and tardiness penalties and took into account finite capacity constraints. Three different sized problems were obtained from a collaborating capital goods company. A design of experimental approach was used to systematically identify that the best genetic operators and GA parameters for each size of problem.

Keywords

References

  1. Aytug, H., Khouja, M., and Vergara, F. E. (2003), Use of genetic algorithm to solve production and operation management problems: a review, International Journal of Production Research, 41, 3955-4009. https://doi.org/10.1080/00207540310001626319
  2. Baker, K. R. (1974), Introduction to Sequencing and Scheduling, Wiley and Sons, New York.
  3. Blazewicz, J., Domschke, W., and Pesch, E. (1996a), The job shop scheduling problem: Conventional and new solution techniques, European Journal of Operational Research, 93, 1-33. https://doi.org/10.1016/0377-2217(95)00362-2
  4. Blazewicz, J., Ecker, K. H., Pesch, E., Schmidt, G., and Weglarz, J. (1996b), Scheduling Computer and Manufacturing Processes, Springer, Berlin.
  5. Blum, C. and Roli, A. (2003), Metaheuristics in combina-torial optimisation: overview and conceptual comparison, ACM Computing Surveys, 35(3), 268-308. https://doi.org/10.1145/937503.937505
  6. Caldeira, J. P. and Agostinho, C. R. (1997), School timetabling using genetic search, Proceedings of the Practice and Theory of Automated Timetabling, University of Toranto, Toronto, 115-122.
  7. Chen, K. and Ji, P. (2007), A mixed integer programming model for advanced planning and scheduling (APS), European Journal of Operational Research, 181(1), 515-522. https://doi.org/10.1016/j.ejor.2006.06.018
  8. Chaudhry, S. S. and Luo, W. (2005), Application of genetic algorithms in production and operation management: a review, International Journal of Production Research, 43, 4083-4101. https://doi.org/10.1080/00207540500143199
  9. Colorni, A., Dorigo, M., and Maniezzo, V. (1998), Metaheuristics for high school timetabling, Computational Optimisation and Application Journal, 9, 277- 298.
  10. Fry, T. D., Oliff, M. D., Minor, E. D., and Leong, G. K. (1989), The effects of product structure and sequencing rule on assembly shop performance, International Journal of Production Research, 27, 671-686. https://doi.org/10.1080/00207548908942575
  11. Gen, M. and Cheng, R. (1997), Genetic Algorithms and Engineering Design, John Wiley and Sons, New York.
  12. Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimisation and Machine Learning, Addison- Wesley, Reading, MA.
  13. Garey, M., Johnson, D., and Sethi, R. (1976), The complexity of flow shop and job shop scheduling, Mathematics of Operations Research, 1(2), 117-129. https://doi.org/10.1287/moor.1.2.117
  14. Graham, R., Lawler, E., Lenstra, J., and Rinnooy K. A. (1979), Optimisation and approximation in deterministic sequencing and scheduling: a survey. Annals of Discrete Mathematics, 5, 287-326. https://doi.org/10.1016/S0167-5060(08)70356-X
  15. He, Y. and Hui, C. W. (2007), Genetic algorithm based on heuristic rules for high-constrained large-size single stage multi-product scheduling with parallel units, Chemical Engineering and Processing, 46(11), 1175 -1191. https://doi.org/10.1016/j.cep.2007.02.023
  16. Hicks, C. and Pongcharoen, P. (2006), Dispatching rules for production scheduling in the capital goods industry, International Journal of Production Economics, 104(1), 154-163. https://doi.org/10.1016/j.ijpe.2005.07.005
  17. King, J. R. and Spackis, A. S. (1980), Scheduling: bibliography and review, International Journal of Physical Distribution and Materials Management, 10, 105-132.
  18. Montgomery, D. C. (2001), Design and Analysis of Experiments, Fifth edition, John Wiley and Sons, NY.
  19. Nagar, A., Haddock, J., and Heragu, S. (1995), Multiple and bicriteria scheduling: a literature survey, European Journal of Operational Research, 81, 88-104. https://doi.org/10.1016/0377-2217(93)E0140-S
  20. Ousterhout, J. K. (1994), Tcl and the Tk Toolkit, Addison Wesley, Massachusetts.
  21. Pongcharoen, P., Stewardson, D. J., Hicks, C., and Braiden, P. M. (2001), Applying designed experiments to optimise the performance of genetic algorithms used for scheduling complex products in the capital goods industry, Journal of Applied Statistics, 28(3-4), 441-455. https://doi.org/10.1080/02664760120034162
  22. Pongcharoen, P., Hicks, C., Braiden, P. M., and Stewardson, D. J. (2002), Determining optimum genetic algorithm parameters for scheduling the manufacturing and assembly of complex products, International Journal of Production Economics, 78(3), 311-322. https://doi.org/10.1016/S0925-5273(02)00104-4
  23. Pongcharoen, P., Hicks, C., and Braiden, P. M. (2004), The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of products structure, European Journal of Operational Research, 152(1), 215-225. https://doi.org/10.1016/S0377-2217(02)00645-8
  24. Reeja, M. K. and Rajendran, C. (2000), Dispatching rules for scheduling in assembly job shops-Part I, International Journal of Production Research, 38(9), 2051 - 2066. https://doi.org/10.1080/002075400188492
  25. Reisman, A., Kumar, A., and Motwani, J. (1994), Flowshop scheduling/sequencing research: a statistical review of the literature, 1952-1994, IEEE Transactions on Engineering Management, 44(3), 316-329. https://doi.org/10.1109/17.618173
  26. Ruiz, R. and Maroto, C. (2005), A comprehensive review and evaluation of permutation flow shop heuristics, European Journal of Operational Research, 165, 479 -494. https://doi.org/10.1016/j.ejor.2004.04.017
  27. Syarif, A., Yun, Y., and Gen, M. (2002), Study on multistage logistic chain network: a spanning tree-based genetic algorithm approach, Computers and Industrial Engineering, 43(1-2), 299-314. https://doi.org/10.1016/S0360-8352(02)00076-1
  28. Todd, D. (1997), Multiple Criteria Genetic Algorithms in Engineering Design and Operation, Ph.D. thesis, Faculty of Engineering, University of Newcastle upon Tyne, UK.
  29. Wichmann, B. A. and Hill, I. D. (1982), Algorithm AS 183 an efficient and portable pseudo-random number generator, Applied Statistics, 31, 188-190. https://doi.org/10.2307/2347988