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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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    1. Antonio Costa & Fulvio Antonio Cappadonna & Sergio Fichera, 2017. "A hybrid genetic algorithm for minimizing makespan in a flow-shop sequence-dependent group scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1269-1283, August.
    2. Ruiz, Ruben & Maroto, Concepcion, 2006. "A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility," European Journal of Operational Research, Elsevier, vol. 169(3), pages 781-800, March.
    3. Feng, Hanxin & Xi, Lifeng & Xiao, Lei & Xia, Tangbin & Pan, Ershun, 2018. "Imperfect preventive maintenance optimization for flexible flowshop manufacturing cells considering sequence-dependent group scheduling," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 218-229.
    4. Pan, Quan-Ke, 2016. "An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling," European Journal of Operational Research, Elsevier, vol. 250(3), pages 702-714.
    5. Cheng-Dar Liou & Yi-Chih Hsieh & Yin-Yann Chen, 2013. "A new encoding scheme-based hybrid algorithm for minimising two-machine flow-shop group scheduling problem," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(1), pages 77-93.
    6. Solimanpur, M. & Vrat, Prem & Shankar, Ravi, 2004. "A heuristic to minimize makespan of cell scheduling problem," International Journal of Production Economics, Elsevier, vol. 88(3), pages 231-241, April.
    7. Liou, Cheng-Dar & Hsieh, Yi-Chih, 2015. "A hybrid algorithm for the multi-stage flow shop group scheduling with sequence-dependent setup and transportation times," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 258-267.
    8. Darat Dechampai & Ladda Tanwanichkul & Kanchana Sethanan & Rapeepan Pitakaso, 2017. "A differential evolution algorithm for the capacitated VRP with flexibility of mixing pickup and delivery services and the maximum duration of a route in poultry industry," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1357-1376, August.
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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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