Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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The Integration of FMS Process Planning and Scheduling Using an Asymmetric Multileveled Symbiotic Evolutionary Algorithm

비대칭형 다계층 공생 진화알고리듬을 이용한 FMS 공정계획과 일정계획의 통합

  • Kim, Yeo Keun (Department of Industrial Engineering, Chonnam National University) ;
  • Kim, Jae Yun (Research Center for High-Quality Electric Components and Systems, Chonnam National University) ;
  • Shin, Kyoung Seok (Department of Industrial Engineering, Chonnam National University)
  • 김여근 (전남대학교 산업공학과) ;
  • 김재윤 (전남대학교 고품질전기전자부품 및 시스템연구센터) ;
  • 신경석 (전남대학교 산업공학과)
  • Published : 2004.06.30
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Abstract

This paper addresses the integrated problem of process planning and scheduling in FMS (Flexible Manufacturing System). The integration of process planning and scheduling is important for an efficient utilization of manufacturing resources. In this paper, a new method using an artificial intelligent search technique, called asymmetric multileveled symbiotic evolutionary algorithm, is presented to handle the two functions at the same time. Efficient genetic representations and operator schemes are considered. While designing the schemes, we take into account the features specific to each of process planning and scheduling problems. The performance of the proposed algorithm is compared with those of a traditional hierarchical approach and existing evolutionary algorithms. The experimental results show that the proposed algorithm outperforms the compared algorithms.

Keywords

Acknowledgement

Supported by : 한국과학재단

References

  1. Alexander, W., Denis, C., Thorn, H., Russell, K., James, W., and Andrew, Z. (1999), Scheduling with alternatives: a link between process planning and scheduling, IIE Transactions, 31, 1093-1102
  2. Bierwirth, C. and Mattfeld, D.C (1999), Production scheduling and rescheduling with genetic algorithms, Evolutionary Computation, 7, 1-17
  3. Campbell, N.A., Mitchell, L.G., and Reece, J.B. (1996), Biology: Concepts & Connections, Second Edition, Benjamin/Cummings Publishing Company Inc.: Redwood City, CA
  4. Capra, F., (1996), The weboflife, Anchor Books: New York
  5. Guerrero, F., Lozano, S., Koltai, T. and Larraneta, J. (1999), Machine loading and part type selection in flexible manufacturing systems, International Journal of Production Research, 37, 1303-1317 https://doi.org/10.1080/002075499191265
  6. Ho, Y.C. and Moodie, C.L. (1996), Solving cell formation problems in a manufacturing environment with flexible processing and routing capabilities, International Journal of Production Research, 34, 2901-2923
  7. Kim, J.Y., Kim, Y.H. and Kim, Y.K. (2001), An endosymbiotic evolutionary algorithm for optimization, Applied Intelligence, 15, 117-130
  8. Kim, Y.K. (2002), A set of data for the integration of process planning and scheduling in FMS, available at http://syslab.chonnam.ac.kr/ links/FMSdata-pp&s.doc
  9. 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
  10. Kim, Y.K., Kim, J.Y. and Lee, W.K. (2003a), A multi-level symbiotic evolutionary algorithm for FMS loading problems with various flexibilities, Journal of the Korean Institute of Industrial Engineers, 29(1), 66-78
  11. Kim, Y.K. and Min, Y.J. (2003), An integrated planning of production and distribution in supply chain management using a multi-level symbiotic evolutionary algorithm, Journal of the Korean Operations Research and Management Science Society, 28(2), 1-15
  12. Kim, Y.K., Park, K. and Ko, J. (2003b), A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling, Computers & Operations Research,30, 1151-1171
  13. Kumar, N. and Shanker, K. (2000), A genetic algorithm for FMS part type selection and machine loading, International Journal of Production Research, 38, 3861-3887
  14. Liu, J. and Maccarthy, B.L. (1997),A global MILP model for FMS scheduling, European Journal of Operational Research, 100, 441-453
  15. Margulis, L. (1970), Origin of eukaryotic cells, Yale University Press: New Haven
  16. Modi, B.K. and Shanker, K. (1994), Models and solution approaches for part movement minimization and load balancing in FMS with machine, tool and process plan flexibilities, International Journal of Production Research,33, 1791-1816
  17. Morad, N. and Zalzala, A. (1999), Genetic algorithms in integrated process planning and scheduling, Journal of Intelligent Manufacturing, 10, 169-179
  18. Paulli, J. (1995), A hierarchical approach for the FM S scheduling problem, European Journal of Operational Research, 86, 32-42
  19. Potter, M.A. (1997), The design and analysis of a computational model of cooperative coevolution, Ph.D. dissertation, George Mason University
  20. Rachamadugu, R. and Stecke, K.E., (1994), Classification and review of FMS scheduling procedures, Production Planning and Control, 5, 2-20.
  21. Sabuncuoglu, I. (1998), A study of scheduling rules of flexible manufacturing systems: a simulation approach, International Journal of Production Research, 36, 527-546
  22. Saygin, C. and Kilic, S.E. (1999), Integrating Flexible Process Plans with Scheduling in Flexible Manufacturing systems, International Journal of Advanced Manufacturing Technology, 15, 268-280
  23. Steck K.E. and Raman N. (1995), FMS planning decisions, operating flexibilities, and system performance, IEEE Transactions on Engineering Management, 42,82-90
  24. Syswerda, G., (1991), A study of reproduction in generational and steady-state genetic algorithms, Foundations of Genetic Algorithms, San Mateo, CA, pp. 94-101