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Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines

Author

Listed:
  • Leilei Meng
  • Chaoyong Zhang
  • Xinyu Shao
  • Yaping Ren
  • Caile Ren

Abstract

This paper investigates an energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines (HFSP-UPM) with the energy-saving strategy of turning off and on. We first analyse the energy consumption of HFSP-UPM and formulate five mixed integer linear programming (MILP) models based on two different modelling ideas namely idle time and idle energy. All the models are compared both in size and computational complexities. The results show that MILP models based on different modelling ideas vary dramatically in both size and computational complexities. HFSP-UPM is NP-Hard, thus, an improved genetic algorithm (IGA) is proposed. Specifically, a new energy-conscious decoding method is designed in IGA. To evaluate the proposed IGA, comparative experiments of different-sized instances are conducted. The results demonstrate that the IGA is more effective than the genetic algorithm (GA), simulating annealing algorithm (SA) and migrating birds optimisation algorithm (MBO). Compared with the best MILP model, the IGA can get the solution that is close to an optimal solution with the gap of no more than 2.17% for small-scale instances. For large-scale instances, the IGA can get a better solution than the best MILP model within no more than 10% of the running time of the best MILP model.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:4:p:1119-1145
    DOI: 10.1080/00207543.2018.1501166
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    Cited by:

    1. Qi Lu & Qi Zhang & Guanghui Zhou, 2023. "Low-Carbon-Driven Product Life-Cycle Process Optimization Framework for Manufacturing Equipment," Sustainability, MDPI, vol. 15(9), pages 1-19, May.
    2. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    3. Hasani, Ali & Hosseini, Seyed Mohammad Hassan, 2020. "A bi-objective flexible flow shop scheduling problem with machine-dependent processing stages: Trade-off between production costs and energy consumption," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    4. Husam Dauod & Nieqing Cao & Debiao Li & Jaehee Kim & Sang Won Yoon & Daehan Won, 2023. "An Order Scheduling Heuristic to Minimize the Total Collation Delays and the Makespan in High-Throughput Make-to-Order Manufacturing Systems," SN Operations Research Forum, Springer, vol. 4(2), pages 1-23, June.
    5. 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.
    6. Xiao, Qinge & Li, Congbo & Tang, Ying & Pan, Jian & Yu, Jun & Chen, Xingzheng, 2019. "Multi-component energy modeling and optimization for sustainable dry gear hobbing," Energy, Elsevier, vol. 187(C).

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