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Effective multiobjective EDA for bi-criteria stochastic job-shop scheduling problem

Author

Listed:
  • Xinchang Hao

    (Waseda University)

  • Mitsuo Gen

    (Tokyo University of Science
    Fuzzy Logic Systems Institute)

  • Lin Lin

    (Fuzzy Logic Systems Institute
    Dalian University of Technology)

  • Gursel A. Suer

    (Ohio University)

Abstract

This paper proposes an effective multiobjective estimation of distribution algorithm (MoEDA) which solves the bi-criteria stochastic job-shop scheduling problem with the uncertainty of processing time. The MoEDA proposal minimizes the expected average makespan and the expected total tardiness within a reasonable amount of computational time. With the framework of proposed MoEDA, the probability model of the operation sequence is estimated firstly. For sampling the processing time of each operation with the Monte Carlo methods, allocation method is used to decide the operation sequence, and then the expected makespan and total tardiness of each sampling are evaluated. Subsequently, updating mechanism of the probability models is proposed according to the best solutions to obtain. Finally, for comparing with some existing algorithms by numerical experiments on the benchmark problems, we demonstrate the proposed effective estimation of distribution algorithm can obtain an acceptable solution in the aspects of schedule quality and computational efficiency.

Suggested Citation

  • Xinchang Hao & Mitsuo Gen & Lin Lin & Gursel A. Suer, 2017. "Effective multiobjective EDA for bi-criteria stochastic job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 28(3), pages 833-845, March.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:3:d:10.1007_s10845-014-1026-0
    DOI: 10.1007/s10845-014-1026-0
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    References listed on IDEAS

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    1. Sanja Petrovic & Carole Fayad & Dobrila Petrovic & Edmund Burke & Graham Kendall, 2008. "Fuzzy job shop scheduling with lot-sizing," Annals of Operations Research, Springer, vol. 159(1), pages 275-292, March.
    2. Golenko-Ginzburg, Dimitri & Gonik, Aharon, 2002. "Optimal job-shop scheduling with random operations and cost objectives," International Journal of Production Economics, Elsevier, vol. 76(2), pages 147-157, March.
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    Cited by:

    1. Lu Sun & Lin Lin & Haojie Li & Mitsuo Gen, 2019. "Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling," Mathematics, MDPI, vol. 7(4), pages 1-20, March.
    2. Xingong Zhang & Win-Chin Lin & Chin-Chia Wu, 2022. "Rescheduling problems with allowing for the unexpected new jobs arrival," Journal of Combinatorial Optimization, Springer, vol. 43(3), pages 630-645, April.
    3. Gabriel Mauricio Zambrano-Rey & Eliana María González-Neira & Gabriel Fernando Forero-Ortiz & María José Ocampo-Monsalve & Andrea Rivera-Torres, 2024. "Minimizing the expected maximum lateness for a job shop subject to stochastic machine breakdowns," Annals of Operations Research, Springer, vol. 338(1), pages 801-833, July.

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