Skip to main content
Log in

A Coevolutionary Algorithm for Balancing and Sequencing in Mixed Model Assembly Lines

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

A mixed model assembly line is a production line where a variety of product models are produced. Line balancing and model sequencing problems are important for an efficient use of such lines. Although the two problems are tightly interrelated with each other, prior researches have considered them separately or sequentially. This paper presents a new method using a coevolutionary algorithm that can solve the two problems at the same time. In the algorithm, it is important to promote population diversity and search efficiency. We adopt a localized interaction within and between populations, and develop methods of selecting symbiotic partners and evaluating fitness. Efficient genetic representations and operator schemes are also provided. When designing the schemes, we take into account the features specific to the problems. Also presented are the experimental results that demonstrate the proposed algorithm is superior to existing approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. N.T. Thomopoulos, “Line balancing-sequencing for mixedmodel assembly,” Management Science, vol. 14, pp. 59–75, 1967.

    Google Scholar 

  2. A.L. Gutjahr and G.L. Nemhauser, “An algorithm for the line balancing problem,” Management Science, vol. 11, pp. 308–315, 1964.

    Google Scholar 

  3. L. Tsai, “Mixed model sequencing to minimize utility work and the risk of conveyor stoppage,” Management Science, vol. 41, pp. 485–495, 1995.

    Google Scholar 

  4. N.T. Thomopoulos, “Mixed model line balancing with smoothed station assignment,” Management Science, vol. 16, pp. 593–603, 1970.

    Google Scholar 

  5. J.L.C. Macaskill, “Production-line balances for mixed-model lines,” Management Science, vol. 19, pp. 423–434, 1972.

    Google Scholar 

  6. A.K. Chakravarty and A. Shtub, “Balancing mixed model lines with in-process inventory,” Management Science, vol. 31, pp. 1161–1174, 1985.

    Google Scholar 

  7. M. Kilbridge and L. Wester, “The assembly line model-mix sequencing problem,” in Proceeding of the 3rd International Conference on Operations Research, Oslo, Paris, 1963, pp. 247–260.

  8. K. Okamura and H. Yamashina, “A heuristic algorithm for the assembly line model-mix sequencing problem to minimize the risk of stopping the conveyor,” International Journal of Production Research, vol. 17, pp. 233–247, 1979.

    Google Scholar 

  9. C.A. Yano and R. Rachamadugu, “Sequencing to minimizework overload in assembly lines with product options,” Management Science, vol. 37, pp. 572–586, 1991.

    Google Scholar 

  10. J.F. Bard, E.M. Dar-El, and A. Shtub, “An analytic framework for sequencing mixed model assembly lines,” International Journal of Production Research, vol. 30, pp. 35–48, 1992.

    Google Scholar 

  11. E.M. Dar-El and A. Navidi, “A mixed-model sequencing application,” International Journal of Production Research, vol. 19, pp. 69–84, 1981.

    Google Scholar 

  12. D.E. Moriarty and R. Miikkulainen, “Forming neural networks through efficient and adaptive coevolution,” Evolutionary Computation, vol. 5, pp. 373–399, 1997.

    Google Scholar 

  13. J.R. Koza, Genetic Programming, The MIT Press: Cambridge, Massachusetts, 1992.

    Google Scholar 

  14. M.A. Potter, “The design and analysis of a computational model of cooperative coevolution,” Ph.D. Dissertation, George Mason University, 1997.

  15. L. Bull and T.C. Fogarty, “Artificial symbiogenesis,” Artificial Life, vol. 2, pp. 269–292, 1995.

    Google Scholar 

  16. W.D. Hillis, “Co-evolving parasites improve simulated evolution as an optimization procedure,” Physica D, vol. 42, pp. 228–234, 1990.

    Google Scholar 

  17. C.D. Rosin and R.K. Belew, “New methods for competitive coevolution,” Evolutionary Computation, vol. 5, pp. 1–29, 1997.

    Google Scholar 

  18. M.L. Maher and J. Poon, “Modelling design exploration as coevolution,” Microcomputers in Civil Engineering, vol. 11, pp. 195–210, 1996.

    Google Scholar 

  19. G. von Laszewski, “Intelligent structural operators for k-way group partitioning problem,” in Proceedings 4th International on Conference Genetic Algorithms, San Mateo, CA, 1991, pp. 45–52.

  20. E. Falkenauer, “A new representation and operators for genetic algorithms applied to grouping problems,” Evolutionary Computation, vol. 2, pp. 123–144, 1994.

    Google Scholar 

  21. Y.K. Kim, C.J. Hyun, and Y. Kim, “Sequencing in mixed model assembly lines: A genetic algorithm approach,” Computers & Operations Research, vol. 23, pp. 1131–1145, 1996.

    Google Scholar 

  22. A.L. Arcus, “An analysis of a computer method of sequencing assembly line operations,” Ph.D. Dissertation, University of California, 1963.

  23. Y.K. Kim, Y.J. Kim, and Y. Kim, “Genetic algorithms for assembly line balancing with various objectives,” Computers & Industrial Engineering, vol. 30, pp. 397–409, 1996.

    Google Scholar 

  24. R.E. Smith, S. Forrest, and A.S. Perelson, “Searching for diverse, cooperative populations with genetic algorithms,” Evolutionary Computation, vol. 1, pp. 127–149, 1993.

    Google Scholar 

  25. S. Forrest, B. Javornik, R.E. Smith, and A.S. Perelson, “Using genetic algorithms to explore pattern recognition in the immune system,” Evolutionary Computation, vol. 1, pp. 191–211, 1993.

    Google Scholar 

  26. Y. Davidor, “A naturally occurring niche and species phenomenon: The model and first results,” in Proceedings 4th International on Conference Genetic Algorithms, San Mateo, CA, 1991, pp. 257–263.

  27. H.J.C. Barbosa, “A coevolutionary genetic algorithm for a game approach to structural optimization,” in Proceedings 7th International on Conference Genetic Algorithms, East Lansing, MI, 1997, pp. 545–552.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kim, Y.K., Kim, J.Y. & Kim, Y. A Coevolutionary Algorithm for Balancing and Sequencing in Mixed Model Assembly Lines. Applied Intelligence 13, 247–258 (2000). https://doi.org/10.1023/A:1026568011013

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1026568011013

Navigation