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A discrete differential evolution algorithm for flow shop group scheduling problem with sequence-dependent setup and transportation times

Author

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  • Shuaipeng Yuan

    (University of Science and Technology Beijing
    Engineering Research Center of MES Technology for Iron and Steel Production, Ministry of Education)

  • Tieke Li

    (University of Science and Technology Beijing
    Engineering Research Center of MES Technology for Iron and Steel Production, Ministry of Education)

  • Bailin Wang

    (University of Science and Technology Beijing
    Engineering Research Center of MES Technology for Iron and Steel Production, Ministry of Education)

Abstract

This study investigates a flow shop group scheduling problem where both sequence-dependent setup time between groups and round-trip transportation time between machines are considered. The objective is to minimize makespan. To solve the problem, we first develop a mixed integer linear programming model and then propose an efficient co-evolutionary discrete differential evolution algorithm (CDDEA). In the CDDEA, several problem-specific heuristic rules are generated to construct initial population. A novel discrete differential evolution mechanism and a cooperative-oriented optimization strategy are proposed to synergistically evolve both the sequence of jobs in each group and the sequence of groups. In addition, two lower bounds are developed to evaluate the solution quality of CDDEA. Extensive computational experiments are carried out. The results show that the proposed CDDEA is effective in solving the studied problem.

Suggested Citation

  • Shuaipeng Yuan & Tieke Li & Bailin Wang, 2021. "A discrete differential evolution algorithm for flow shop group scheduling problem with sequence-dependent setup and transportation times," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 427-439, February.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01580-3
    DOI: 10.1007/s10845-020-01580-3
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    References listed on IDEAS

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    Cited by:

    1. Yiying Zhang & Aining Chi, 2023. "Group teaching optimization algorithm with information sharing for numerical optimization and engineering optimization," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1547-1571, April.
    2. Wang, Yuhang & Han, Yuyan & Wang, Yuting & Tasgetiren, M. Fatih & Li, Junqing & Gao, Kaizhou, 2023. "Intelligent optimization under the makespan constraint: Rapid evaluation mechanisms based on the critical machine for the distributed flowshop group scheduling problem," European Journal of Operational Research, Elsevier, vol. 311(3), pages 816-832.

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