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

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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.

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Acknowledgments

This work is partly supported by the Japan Society of Promotion of Science (JSPS): Grant-in-Aid for Scientific Research (C) (No. 24510219.0001), the Fundamental Research Funds (Software+X) of Dalian University of Technology (Nos. DUT12JR05, DUT12JR12), and supported by New Teacher Fund of Ministry of Education of China (No. 20120041120053).

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Authors and Affiliations

  1. Waseda University, Kitakyushu, Japan

    Xinchang Hao

  2. Tokyo University of Science, Tokyo, Japan

    Mitsuo Gen

  3. Fuzzy Logic Systems Institute, Kitakyushu, Japan

    Mitsuo Gen & Lin Lin

  4. Dalian University of Technology, Dalian, China

    Lin Lin

  5. Ohio University, Athens, OH, USA

    Gursel A. Suer

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  1. Xinchang Hao
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Correspondence to Mitsuo Gen.

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Hao, X., Gen, M., Lin, L. et al. Effective multiobjective EDA for bi-criteria stochastic job-shop scheduling problem. J Intell Manuf 28, 833–845 (2017). https://doi.org/10.1007/s10845-014-1026-0

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