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Matheuristics pp 189-208 | Cite as

MIP-based GRASP and Genetic Algorithm for Balancing Transfer Lines

  • Alexandre Dolgui
  • Anton Eremeev
  • Olga Guschinskaya
Chapter
Part of the Annals of Information Systems book series (AOIS, volume 10)

Abstract

Abstract In this chapter, we consider a problem of balancing transfer lines with multi-spindle machines. The problem has a number of distinct features in comparison with the well-studied assembly line balancing problem, such as parameterized operation times, non-strict precedence constraints, and parallel operations execution. We propose a mixed-integer programming (MIP)-based greedy randomized adaptive search procedure (GRASP) and a genetic algorithm (GA) for this problem using a MIP formulation. Both algorithms are implemented in GAMS using the CPLEX MIP solver and compared to problem-specific heuristics on randomly generated instances of different types. The results of computational experiments indicate that on large-scale problem instances the proposed methods have an advantage over the methods from literature for finding high quality solutions. The MIP-based recombination operator that arranges the elements of parent solutions in the best possible way is shown to be useful in the GA.

Keywords

Genetic Algorithm Greedy Randomize Adaptive Search Procedure Assembly Line Balance Assembly Line Balance Problem Greedy Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

Acknowledgements

The authors thank Michael R. Bussieck for helpful discussions on better usage of GAMS potential. The research is supported by the Russian Foundation for Basic Research, grant 07-01-00410.

References

  1. 1.
    I. Baybars. A survey of exact algorithms for the simple assembly line balancing. Management Science, 32:909–932, 1986.CrossRefGoogle Scholar
  2. 2.
    C. Becker and A. Scholl. A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research, 168:694–715, 2006.CrossRefGoogle Scholar
  3. 3.
    P. Borisovsky, A. Dolgui, and A. Eremeev. Genetic algorithms for a supply management problem: MIP-recombination vs. greedy decoder. European Journal of Operational Research, 195:770–779, 2009.CrossRefGoogle Scholar
  4. 4.
    A. Dolgui, B. Finel, F. Vernadat, N. Guschinsky, and G. Levin. A heuristic approach for transfer lines balancing. Journal of Intelligent Manufacturing, 16:159–172, 2005.CrossRefGoogle Scholar
  5. 5.
    A. Dolgui, B. Finel, N. Guschinsky, G. Levin, and F. Vernadat. MIP approach to balancing transfer lines with blocks of parallel operations. IIE Transactions, 38:869–882, 2006.CrossRefGoogle Scholar
  6. 6.
    A. Dolgui, N. Guschinsky, and G. Levin. A special case of transfer lines balancing by graph approach. European Journal of Operational Research, 168:732–746, 2006.CrossRefGoogle Scholar
  7. 7.
    A. Eremeev. On complexity of optimal recombination for binary representations of solutions. Evolutionary Computation, 16:127–147, 2008.CrossRefGoogle Scholar
  8. 8.
    P. Festa and M.G.C. Resende. GRASP: An annotated bibliography. In C.C. Ribeiro and P. Hansen, editors, Essays and surveys on metaheuristics, pages 325–367. Kluwer, Boston, 2001.Google Scholar
  9. 9.
    S. Ghosh and R. Gagnon. A comprehensive literature review and analysis of the design, balancing and scheduling of assembly lines. International Journal of Production Research, 27(4):637–670, 1989.CrossRefGoogle Scholar
  10. 10.
    O. Guschinskaya and A. Dolgui. A comparative evaluation of exact and heuristic methods for transfer lines balancing problem. In A. Dolgui, G. Morel, and C. Pereira, editors, Information Control Problems in Manufacturing 2006: A Proceedings volume from the 12th IFAC International Symposium, volume2, pages 395–400. Elsevier, 2006.Google Scholar
  11. 11.
    O. Guschinskaya and A. Dolgui. Balancing transfer lines with multiple-spindle machines using GRASP. Unpublished manuscript, 2007.Google Scholar
  12. 12.
    O. Guschinskaya, A. Dolgui, N. Guschinsky, and G. Levin. A heuristic multi-start decomposition approach for optimal design of serial machining lines. European Journal of Operational Research, 189:902–913, 2008.CrossRefGoogle Scholar
  13. 13.
    J.P. Hart and A.W. Shogan. Semi-greedy heuristics: An empirical study. Operations Research Letters, 6:107–114, 1987.CrossRefGoogle Scholar
  14. 14.
    J. Holland. Adaptation in natural and artificial systems. University of Michigan Press, 1975.Google Scholar
  15. 15.
    C.R. Reeves. Genetic algorithms for the operations researcher. INFORMS Journal on Computing, 9(3):231–250, 1997.CrossRefGoogle Scholar
  16. 16.
    M.G.C. Resende and C.C. Ribeiro. Greedy randomized adaptive search procedures. In F. Glover and G. Kochenberger, editors, Handbook of Metaheuristics, pages 219–249. Kluwer Academic Publishers, 2003.Google Scholar
  17. 17.
    A. Scholl. Balancing and sequencing of assembly lines. Physica, Heidelberg, 1999.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Alexandre Dolgui
    • 1
  • Anton Eremeev
    • 2
  • Olga Guschinskaya
    • 1
  1. 1.Ecole Nationale Supérieure des Mines de Saint EtienneSaint EtienneFrance
  2. 2.Omsk Branch of Sobolev Institute of Mathematics SB RASOmskRussia

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