Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
권호 표지
DOI QR코드

DOI QR Code

An Improved Genetic Algorithm for Integrated Planning and Scheduling Algorithm Considering Tool Flexibility and Tool Constraints

공구유연성과 공구관련제약을 고려한 통합공정일정계획을 위한 유전알고리즘

  • Kim, Young-Nam (Department of computer science and engineering, Pohang university of Science and Technology) ;
  • Ha, Chunghun (School of Information & Computer Engineering, Hongik University)
  • 김영남 (POSTECH 컴퓨터공학과) ;
  • 하정훈 (홍익대학교 정보컴퓨터공학부 산업공학전공)
  • Received : 2017.05.17
  • Accepted : 2017.06.20
  • Published : 2017.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes an improved standard genetic algorithm (GA) of making a near optimal schedule for integrated process planning and scheduling problem (IPPS) considering tool flexibility and tool related constraints. Process planning involves the selection of operations and the allocation of resources. Scheduling, meanwhile, determines the sequence order in which operations are executed on each machine. Due to the high degree of complexity, traditionally, a sequential approach has been preferred, which determines process planning firstly and then performs scheduling independently based on the results. The two sub-problems, however, are complicatedly interrelated to each other, so the IPPS tend to solve the two problems simultaneously. Although many studies for IPPS have been conducted in the past, tool flexibility and capacity constraints are rarely considered. Various meta-heuristics, especially GA, have been applied for IPPS, but the performance is yet satisfactory. To improve solution quality against computation time in GA, we adopted three methods. First, we used a random circular queue during generation of an initial population. It can provide sufficient diversity of individuals at the beginning of GA. Second, we adopted an inferior selection to choose the parents for the crossover and mutation operations. It helps to maintain exploitation capability throughout the evolution process. Third, we employed a modification of the hybrid scheduling algorithm to decode the chromosome of the individual into a schedule, which can generate an active and non-delay schedule. The experimental results show that our proposed algorithm is superior to the current best evolutionary algorithms at most benchmark problems.

Keywords

References

  1. Atashpaz-Gargari, E. and Lucas, C., Imperialist competitive algorithm : An algorithm for optimization inspired by imperialistic competition, 2007 IEEE Congress on Evolutionary Computation, CEC 2007, 2007, pp. 4661-4667.
  2. Ausaf, M.F., Li, X., and Gao, L., Optimization Algorithms for Integrated Process Planning and Scheduling Problem-A Survey, 2014, pp. 5278-5283.
  3. Bierwirth, C. and Mattfeld, D.C., Production scheduling and rescheduling with genetic algorithms, Evolutionary computation, 1999, Vol. 7, No. 1, pp. 1-17. https://doi.org/10.1162/evco.1999.7.1.1
  4. Blanton Jr, J.L. and Wainwright, R.L., Multiple vehicle routing with time and capacity constraint using genetic algorithms, Proceedings of the Fifth International Conference on Genetic Algorithms, 1993, pp. 452-459.
  5. Chaudhry, I.A. and Khan, A.A., A research survey : Review of flexible job shop scheduling techniques, International Transactions in Operational Research, 2016, Vol. 23, No. 3, pp. 551-591. https://doi.org/10.1111/itor.12199
  6. Cho, S., Lee, H., and Kim, S., A Study on Dynamic Scheduling in Flexible Manufacturing System Environment, Journal of the Society of Korea Industrial and Systems Engineering, 2004, Vol. 27, No. 2, pp. 17-23.
  7. Cinar, D., Oliveira, J.A., Topcu, Y.I., and Pardalos, P.M., A priority-based genetic algorithm for a flexible job shop scheduling problem, Journal of Industrial and Management Optimization, 2016, Vol. 12, No. 4, pp. 1391-1415. https://doi.org/10.3934/jimo.2016.12.1391
  8. Giffler, B. and Thompson, G.L., Algorithms for Solving Production-Scheduling Problems, Operations Research, 1960, Vol. 8, No. 4, pp. 487-503. https://doi.org/10.1287/opre.8.4.487
  9. Ho, Y.-C. and Moodie, C.L., Solving cell formation problems in a manufacturing environment with flexible processing and routeing capabilities, International Journal of Production Research, 1996, Vol. 34, No. 10, pp. 2901-2923. https://doi.org/10.1080/00207549608905065
  10. Kim, Y.K., Kim, J.Y., and Shin, K.S., An asymmetric multileveled symbiotic evolutionary algorithm for integrated FMS scheduling, Journal of Intelligent Manufacturing, 2007, Vol. 18, No. 6, pp. 631-645. https://doi.org/10.1007/s10845-007-0037-5
  11. Kim, Y.K., Park, K., and Ko, J., A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling, Computers & Operations Research, 2003, Vol. 30, No. 8, pp. 1151-1171. https://doi.org/10.1016/S0305-0548(02)00063-1
  12. Lee, D., Applying tabu search to multiprocessor task scheduling problem with precedence relations, Journal of the Society of Korea Industrial and Systems Engineering, 2004, Vol. 27, No. 4, pp. 1-6.
  13. Li, X., Gao, L., and Shao, X., An active learning genetic algorithm for integrated process planning and scheduling, Expert Systems with Applications, 2012, Vol. 39, No. 8, pp. 6683-6691. https://doi.org/10.1016/j.eswa.2011.11.074
  14. Li, X., Gao, L., Shao, X., Zhang, C., and Wang, C., Mathematical modeling and evolutionary algorithmbased approach for integrated process planning and scheduling, Computers & Operations Research, 2010, Vol. 37, No. 4, pp. 656-667. https://doi.org/10.1016/j.cor.2009.06.008
  15. Li, X., Shao, X., Gao, L., and Qian, W., An effective hybrid algorithm for integrated process planning and scheduling, International Journal of Production Economics, 2010, Vol. 126, No. 2, pp. 289-298. https://doi.org/10.1016/j.ijpe.2010.04.001
  16. Lian, K., Zhang, C., Gao, L., and Li, X., Integrated process planning and scheduling using an imperialist competitive algorithm, International Journal of Production Research, 2012, Vol. 5015, No. 15, pp. 4326-4343.
  17. Liu, M., Sun, Z.J., Yan, J.W., and Kang, J.S., An adaptive annealing genetic algorithm for the job-shop planning and scheduling problem, Expert Systems with Applications, 2011, Vol. 38, No. 8, pp. 9248-9255. https://doi.org/10.1016/j.eswa.2011.01.136
  18. MORAD, N. and A. Zalzala, Genetic algorithms in integrated process planning and scheduling, Journal of Intelligent Manufacturing, 1999, Vol. 10, No. 2, pp. 169-179. https://doi.org/10.1023/A:1008976720878
  19. Nasr, N. and Elsayed, E.A., Job shop scheduling with alternative machines, International Journal of Production Research, 1990, Vol. 28, No. 9, pp. 1595-1609. https://doi.org/10.1080/00207549008942818
  20. Park, B.J. and Lee, S.W., Job Shop Scheduling with Evolutionary Algorithms, Journal of The Korean Institute of Plant Engineering, 2000, Vol. 5, No. 2, pp. 95-102.
  21. Petrovic, M., Vukovic, N., Mitic, M., and Miljkovic, Z., Integration of process planning and scheduling using chaotic particle swarm optimization algorithm, Expert Systems with Applications, 2016, Vol. 64, pp. 569-588. https://doi.org/10.1016/j.eswa.2016.08.019
  22. Shao, X.Y., Li, X.Y., Gao, L., and Zhang, C.Y., Integration of process planning and scheduling-A modified genetic algorithm-based approach, Journal of Computers & Operations Research, 2009, Vol. 36, No. 6, pp. 2082-2096. https://doi.org/10.1016/j.cor.2008.07.006
  23. Thomalla, C.S., Job shop scheduling with alternative process plans, International Journal of Production Economics, 2001, Vol. 74, No. 1-3, pp. 125-134. https://doi.org/10.1016/S0925-5273(01)00119-0
  24. Zhang, L. and Wong, T.N., An object-coding genetic algorithm for integrated process planning and scheduling, European Journal of Operational Research, 2015, Vol. 244, No. 2, pp. 434-444. https://doi.org/10.1016/j.ejor.2015.01.032
  25. Zhang, S., Yu, Z., Zhang, W., Yu, D., and Xu, Y., An Extended Genetic Algorithm for Distributed Integration of Fuzzy Process Planning and Scheduling, Mathematical Problems in Engineering, 2016, Vol. 2016, pp. 1-13.