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An MILP Model for Energy-Conscious Flexible Job Shop Problem with Transportation and Sequence-Dependent Setup Times

Author

Listed:
  • Leilei Meng

    (School of Computer Science, Liaocheng University, Liaocheng 252059, China)

  • Biao Zhang

    (School of Computer Science, Liaocheng University, Liaocheng 252059, China)

  • Kaizhou Gao

    (School of Computer Science, Liaocheng University, Liaocheng 252059, China)

  • Peng Duan

    (School of Computer Science, Liaocheng University, Liaocheng 252059, China)

Abstract

As environmental awareness grows, energy-aware scheduling is attracting increasing attention. Compared with traditional flexible job shop scheduling problem (FJSP), FJSP, with considering sequence-dependent setup times and transportation times (FJSP-SDST-T), is closer to real production. In existing research, little research has focused on FJSP-SDST-T with the minimization energy consumption. In order to make up the gap, a mixed integer linear programming (MILP) model has been formulated to solve FJSP-SDST-T with minimizing energy. Firstly, the total energy consumption of the workshop included the processing energy consumption, setup energy consumption, idle energy consumption, transportation energy consumption and common energy consumption, which were analyzed and formulated by introducing related decision variables. Then, the MILP model was detailedly formulated from the formulation of the energy consumption composition, the objective function, the decision variables and the constraint sets and the linearization. Finally, experiments were carried out on extended benchmark cases and the results showed the effectiveness of the MILP model.

Suggested Citation

  • Leilei Meng & Biao Zhang & Kaizhou Gao & Peng Duan, 2022. "An MILP Model for Energy-Conscious Flexible Job Shop Problem with Transportation and Sequence-Dependent Setup Times," Sustainability, MDPI, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:776-:d:1021886
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    References listed on IDEAS

    as
    1. Leilei Meng & Chaoyong Zhang & Xinyu Shao & Yaping Ren & Caile Ren, 2019. "Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines," International Journal of Production Research, Taylor & Francis Journals, vol. 57(4), pages 1119-1145, February.
    2. Shen, Liji & Dauzère-Pérès, Stéphane & Neufeld, Janis S., 2018. "Solving the flexible job shop scheduling problem with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 265(2), pages 503-516.
    3. Tianhua Jiang & Chao Zhang & Huiqi Zhu & Jiuchun Gu & Guanlong Deng, 2018. "Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm," Mathematics, MDPI, vol. 6(11), pages 1-16, October.
    4. Xiuli Wu & Xianli Shen & Qi Cui, 2018. "Multi-Objective Flexible Flow Shop Scheduling Problem Considering Variable Processing Time due to Renewable Energy," Sustainability, MDPI, vol. 10(3), pages 1-30, March.
    5. Zhang, Liping & Tang, Qiuhua & Wu, Zhengjia & Wang, Fang, 2017. "Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops," Energy, Elsevier, vol. 138(C), pages 210-227.
    6. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    7. Tianhua Jiang & Chao Zhang & Huiqi Zhu & Guanlong Deng, 2018. "Energy-Efficient Scheduling for a Job Shop Using Grey Wolf Optimization Algorithm with Double-Searching Mode," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-12, October.
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