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Novel approach to energy-efficient flexible job-shop scheduling problems

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Listed:
  • Rakovitis, Nikolaos
  • Li, Dan
  • Zhang, Nan
  • Li, Jie
  • Zhang, Liping
  • Xiao, Xin

Abstract

In this work, we develop a novel mathematical formulation for the energy-efficient flexible job-shop scheduling problem using the improved unit-specific event-based time representation. The flexible job-shop is represented using the state-task network. It is shown that the proposed model is superior to the existing models with the same or better solutions by up to 13.5 % energy savings in less computational time. Furthermore, it can generate feasible solutions for large-scale instances that the existing models fail to solve. To efficiently solve large-scale problems, a grouping-based decomposition approach is proposed to divide the entire problem into smaller subproblems. It is demonstrated that the proposed decomposition approach can generate good feasible solutions with reduced energy consumption for large-scale examples in significantly less computational time (within 10 min). It can achieve up to 43.1 % less energy consumption in comparison to the existing gene-expression programming-based algorithm.

Suggested Citation

  • Rakovitis, Nikolaos & Li, Dan & Zhang, Nan & Li, Jie & Zhang, Liping & Xiao, Xin, 2022. "Novel approach to energy-efficient flexible job-shop scheduling problems," Energy, Elsevier, vol. 238(PB).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221020211
    DOI: 10.1016/j.energy.2021.121773
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    References listed on IDEAS

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    Cited by:

    1. João M. R. C. Fernandes & Seyed Mahdi Homayouni & Dalila B. M. M. Fontes, 2022. "Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review," Sustainability, MDPI, vol. 14(10), pages 1-34, May.
    2. Ying Tian & Zhanxu Gao & Lei Zhang & Yujing Chen & Taiyong Wang, 2023. "A Multi-Objective Optimization Method for Flexible Job Shop Scheduling Considering Cutting-Tool Degradation with Energy-Saving Measures," Mathematics, MDPI, vol. 11(2), pages 1-31, January.
    3. Müller, David & Müller, Marcus G. & Kress, Dominik & Pesch, Erwin, 2022. "An algorithm selection approach for the flexible job shop scheduling problem: Choosing constraint programming solvers through machine learning," European Journal of Operational Research, Elsevier, vol. 302(3), pages 874-891.
    4. M. Hajibabaei & J. Behnamian, 2023. "Fuzzy cleaner production in assembly flexible job-shop scheduling with machine breakdown and batch transportation: Lagrangian relaxation," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-26, July.

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