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Research on flexible job-shop scheduling problem in green sustainable manufacturing based on learning effect

Author

Listed:
  • Zhao Peng

    (Wuhan University of Technology)

  • Huan Zhang

    (Wuhan University of Technology)

  • Hongtao Tang

    (Wuhan University of Technology)

  • Yue Feng

    (Wuhan University of Technology)

  • Weiming Yin

    (Wuhan University of Technology)

Abstract

As one of the manufacturing industries with high energy consumption and high pollution, sand casting is facing major challenges in green manufacturing. In order to balance production and green sustainable development, this paper puts forward man–machine dual resource constraint mechanism. In addition, a multi-objective flexible job shop scheduling problem model constrained by job transportation time and learning effect is constructed, and the goal is to minimize processing time energy consumption and noise. Subsequently, a hybrid discrete multi-objective imperial competition algorithm (HDMICA) is developed to solve the model. The global search mechanism based on the HDMICA improves two aspects: a new initialization method to improve the quality of the initial population, and the empire selection method based on Pareto non-dominated solution to balance the empire forces. Then, the improved simulated annealing algorithm is embedded in imperial competition algorithm (ICA), which overcomes the premature convergence problem of ICA. Therefore, four neighborhood structures are designed to help the algorithm jump out of the local optimal solution. Finally, an example is used to verify the feasibility of the proposed algorithm. By comparing with the original ICA and other four algorithms, the effectiveness of the proposed algorithm in the quality of the first frontier solution is verified.

Suggested Citation

  • Zhao Peng & Huan Zhang & Hongtao Tang & Yue Feng & Weiming Yin, 2022. "Research on flexible job-shop scheduling problem in green sustainable manufacturing based on learning effect," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1725-1746, August.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:6:d:10.1007_s10845-020-01713-8
    DOI: 10.1007/s10845-020-01713-8
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    References listed on IDEAS

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