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A Pareto block-based estimation and distribution algorithm for multi-objective permutation flow shop scheduling problem

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  • Anurag Tiwari
  • Pei-Chann Chang
  • M.K. Tiwari
  • Nevin John Kollanoor

Abstract

Multi-objective flow shop scheduling plays a key role in real-life scheduling problem which attract the researcher attention. The primary concern is to find the best sequence for flow shop scheduling problem. Estimation of Distribution Algorithms (EDAs) has gained sufficient attention from the researchers and it provides prominent results as an alternate of traditional evolutionary algorithms. In this paper, we propose the pareto optimal block-based EDA using bivariate model for multi-objective flow shop scheduling problem. We apply a bivariate probabilistic model to generate block which have the better diversity. We employ the non-dominated sorting technique to filter the solutions. To check the performance of proposed approach, we test it on the benchmark problems available in OR-library and then we compare it with non-dominated sorting genetic algorithm-II (NSGA-II). Computational results show that pareto optimal BBEDA provides better result and better convergence than NSGA-II.

Suggested Citation

  • Anurag Tiwari & Pei-Chann Chang & M.K. Tiwari & Nevin John Kollanoor, 2015. "A Pareto block-based estimation and distribution algorithm for multi-objective permutation flow shop scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 53(3), pages 793-834, February.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:3:p:793-834
    DOI: 10.1080/00207543.2014.933273
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    Cited by:

    1. Alvarez-Meaza, Izaskun & Zarrabeitia-Bilbao, Enara & Rio-Belver, Rosa-María & Garechana-Anacabe, Gaizka, 2021. "Green scheduling to achieve green manufacturing: Pursuing a research agenda by mapping science," Technology in Society, Elsevier, vol. 67(C).
    2. Choo Jun Tan & Siew Chin Neoh & Chee Peng Lim & Samer Hanoun & Wai Peng Wong & Chu Kong Loo & Li Zhang & Saeid Nahavandi, 2019. "Application of an evolutionary algorithm-based ensemble model to job-shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 879-890, February.

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