Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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A Multiobjective Process Planning of Flexible Assembly Systems with Evolutionary Algorithms

진화알고리듬을 이용한 유연조립시스템의 다목적 공정계획

  • Shin, Kyoung Seok (Department of Industrial Engineering, Chonnam National University) ;
  • Kim, Yeo Keun (Department of Industrial Engineering, Chonnam National University)
  • 신경석 (전남대학교 산업공학과) ;
  • 김여근 (전남대학교 산업공학과)
  • Published : 2005.09.30
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Abstract

This paper deals with a multiobjective process planning problem of flexible assembly systems(FASs). The FAS planning problem addressed in this paper is an integrated one of the assignment of assembly tasks to stations and the determination of assembly routing, while satisfying precedence relations among the tasks and flexibility capacity for each station. In this research, we consider two objectives: minimizing transfer time of the products among stations and absolute deviation of workstation workload(ADWW). We place emphasis on finding a set of diverse near Pareto or true Pareto optimal solutions. To achieve this, we present a new multiobjective coevolutionary algorithm for the integrated problem here, named a multiobjective symbiotic evolutionary algorithm(MOSEA). The structure of the algorithm and the strategies of evolution are devised in this paper to enhance the search ability. Extensive computational experiments are carried out to demonstrate the performance of the proposed algorithm. The experimental results show that the proposed algorithm is a promising method for the integrated and multiobjective problem.

Keywords

Acknowledgement

Supported by : 전남대학교

References

  1. Ammons, J.C., Lofgren, C.B. and McGinnis, L.F.(1985), A large scale machine loading problem in flexible assembly, Annals of Operations Research, 3, 319-332
  2. Coello, C.A.C.(1999), A Comprehensive Survey of Evolutionary Based Multiobjective Optimization Techniques, Knowledge andInformation Systems, 1(3), 269-308 https://doi.org/10.1007/BF03325101
  3. Deb, K.(1999), Multi objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems, Evolutionary Computation, 7(3), 205-230 https://doi.org/10.1162/evco.1999.7.3.205
  4. Deb, K., Agrawal, S., Pratap, A. and Meyarivan, T.(2000), A Fast Elitist Non Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II, In M.S. et al.(Ed.), Parallel Problem Solving from Nature-PPSN VI, Berlin, pp. 849-858. Springer
  5. Fonseca, C.M. and Fleming, P.J.(1993), Genetic algorithm for multiobjective optimization, formulation, discussion and generalization, In Forrest, S. (ed.) Genetic Algorithms: Proceeding of the Fifth International Conference, Morgan Kaufmann, San Mateo, CA, 416-423
  6. Goldberg, D.E.(1989), Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley, Reading, Massachusetts
  7. Groover, M.P.(2001), Automation, production systems and computer integrated manufacturing, Prentice-Hall
  8. Horn, J., Nafpliotis, N. and Goldberg D.E.(1994), A niched Pareto genetic algorithm for multiobjective optimization. IEEE international Conference on Evolutionary Computation, 1, 82-87
  9. Hyun, C.J., Kim, Y.H. and Kim, Y.K.(1998), A genetic algorithm for multiple objective sequencing problems in mixed model assembly. Computers & Operations Research, 25, 657-690
  10. Khouja, M., Booth, D.E., Suh, M. and Mahaney, J.K.(2000), Statistical procedures for task assignment and robot selection in assembly cells, International Journal of Computer Integrated Manufacturing, 13, 95-106 https://doi.org/10.1080/095119200129957
  11. Kilbridge, M.D. and Wester, L.(1964), A heuristic method of assembly line balancing, Journal of Industrial Engineering, 12
  12. Kim, Y.K. and Kwak, J.S.(1993), Mixed model assembly line balancing with the related task consideration, Journal of the Korean Operations Research and Management Science Society, 18(2), 1-22
  13. Kim, Y.K., Euy, J.M., Shin, K.S. and Kim, Y.J.(2004), Process Planning in Flexible Assembly Systems Using a Symbiotic Evolutionary Algorithm, IE Interfaces, 17(2), 208-217
  14. Kim, Y.K., Kim, J.Y. and Kim, Y. (2000), A coevolutionary algorithm for balancing and sequencing in mixed model assembly lines, Applied Intelligence, 13, 247-258 https://doi.org/10.1023/A:1026568011013
  15. Kim, J.Y., Kim, Y. and Kim, Y.K.(2001), An endosymbiotic evolutionary algorithm for optimization, Applied Intelligence, 15, 117-130 https://doi.org/10.1023/A:1011279221489
  16. Kim, Y.K., Kim, S.J., Kim, J.Y. and Kwak, J.S.(1999), A Coevolutionary Algorithm for Balancing aand Sequencing Mixed Model U-Lines, Journal of the Korea Institute of Industrial Engineers, 25(4), 411-420
  17. Kim, Y.K., Yun, B.S. and Lee, S.B. (1997a), Metaheuristics, Yeongji Moonhwasa, Seoul, Korea
  18. Kim, Y.K., Lee, S.Y., Kim, Y.J.(1997b), A Genetic Algorithm for Improving Workload Smoothness in Mixed Model Assembly Lines, Journal of the Korea Institute of Industrial Engineers, 23(3), 515-532
  19. Knowles, J.D. and Corne, D.W.(1999), The Pareto archived evolution strategy: A new baseline algorithm for multi-objective optimization. IEEE International Conference on Evolutionary Computation, 98-105
  20. Lee, H.F. and Johnson, R.V.(1991), A Line Balancing strategy for designing flexible assembly systems, The International Journal of Flexible Manufacturing Systems, 3, 91-120 https://doi.org/10.1007/BF00167513
  21. Lee, H.F. and Stecke, K.E.(1996), An integrated design support method for flexible assembly system, Journal of Manufacturing Systems, 15, 13-32 https://doi.org/10.1016/0278-6125(96)84212-9
  22. Lucertini, M., Pacciarelli, D. and Pacifici, A.(1996), Optimal flow management in flexible assembly systems: The minimal part transfer problem, Systems Science, 22(2), 69-80
  23. Markus, D. and Robert, J.G.(1989), Flexible assembly systems; test result for an approach for near real time scheduling and routing of multiple products, International Journal of Production Research, 27, 215-227 https://doi.org/10.1080/00207548908942543
  24. Moriarty, D.E. and Miikkulainen, R.(1997), Forming neural networks through efficient and adaptive coevolution, Evolutionary Computation, 5, 373-399 https://doi.org/10.1162/evco.1997.5.4.373
  25. Park, M.W. and Kim, Y.D.(1995), A heuristic for setting up a flexible assembly system, International Journal of Production Research, 33, 2295-2310 https://doi.org/10.1080/00207549508904816
  26. Potter, M.A.(1997), The design and analysis of a computational model of cooperative coevolution, Ph.D. dissertation, George Mason University
  27. Sawik, T.(1997), An interactive approach to bicriterion loading of a flexible assembly system, Mathematical and Computer Modeling, 25, 71-83
  28. Sawik, T.(1999), Production planning and scheduling in flexible assembly systems, Springer-Verlag, Berlin
  29. Sawik, T.(2000), An LP based approach for loading and routing in a flexible assembly line, International Journal of Production Economics, 64, 49-58 https://doi.org/10.1016/S0925-5273(99)00043-2
  30. Schaffer, J.D.(1985), Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, In Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, 93-100, Lawrence Erlbaum
  31. Shin, K. S.(2005), A multi objective process planning of flexible assembly systems with evolutionary algorithms, available at http://syslab.chonnam.ac.kr/links/M-FASData.doc
  32. Srinivas, N. and Deb, K.(1994), Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation, 2(3), 221-248 https://doi.org/10.1162/evco.1994.2.3.221
  33. Tan, K.C., LEE, T.H. and Khor, E.F.(2002), Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparison, Artificial Intelligence Review, 17, 253-290
  34. Thomopoulos, N.T.(1967), Line balancing-sequencing for mixed model assembly, Management Science, 14, 59-75
  35. Zitzler, E. and Thiele L.(1999), Mutlobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4),257-271 https://doi.org/10.1109/4235.797969
  36. Zitzler, E., Laumanns M. and Thiele, L.(2001), SPEA2: Improving the Strength Pareto evolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CR-8092 Zurich, Switzerland, May