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An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production

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  • Shijin Wang
  • Xiaodong Wang
  • Feng Chu
  • Jianbo Yu

Abstract

Energy-efficient scheduling is highly necessary for energy-intensive industries, such as glass, mould or chemical production. Inspired by a real-world glass-ceramics production process, this paper investigates a bi-criteria energy-efficient two-stage hybrid flow shop scheduling problem, in which parallel machines with eligibility are at stage 1 and a batch machine is at stage 2. The performance measures considered are makespan and total energy consumption. Time-of-use (TOU) electricity prices and different states of machines (working, idle and turnoff) are integrated. To tackle this problem, a mixed integer programming (MIP) is formulated, based on which an augmented ε-constraint (AUGMECON) method is adopted to obtain the exact Pareto front. A problem-tailored constructive heuristic method with local search strategy, a bi-objective tabu search algorithm and a bi-objective ant colony optimisation algorithm are developed to deal with medium- and large-scale problems. Extensive computational experiments are conducted, and a real-world case is solved. The results show effectiveness of the proposed methods, in particular the bi-objective tabu search.

Suggested Citation

  • Shijin Wang & Xiaodong Wang & Feng Chu & Jianbo Yu, 2020. "An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production," International Journal of Production Research, Taylor & Francis Journals, vol. 58(8), pages 2283-2314, April.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:8:p:2283-2314
    DOI: 10.1080/00207543.2019.1624857
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    Citations

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    Cited by:

    1. Andrzej Bożek, 2020. "Energy Cost-Efficient Task Positioning in Manufacturing Systems," Energies, MDPI, vol. 13(19), pages 1-21, September.
    2. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2023. "Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1091-1109.
    3. Heydar, Mojtaba & Mardaneh, Elham & Loxton, Ryan, 2022. "Approximate dynamic programming for an energy-efficient parallel machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 302(1), pages 363-380.
    4. Arshad Ali & Yuvraj Gajpal & Tarek Y. Elmekkawy, 2021. "Distributed permutation flowshop scheduling problem with total completion time objective," OPSEARCH, Springer;Operational Research Society of India, vol. 58(2), pages 425-447, June.
    5. An, Xiangxin & Si, Guojin & Xia, Tangbin & Wang, Dong & Pan, Ershun & Xi, Lifeng, 2023. "An energy-efficient collaborative strategy of maintenance planning and production scheduling for serial-parallel systems under time-of-use tariffs," Applied Energy, Elsevier, vol. 336(C).
    6. Neufeld, Janis S. & Schulz, Sven & Buscher, Udo, 2023. "A systematic review of multi-objective hybrid flow shop scheduling," European Journal of Operational Research, Elsevier, vol. 309(1), pages 1-23.
    7. Hsin-Chieh Wu & Horng-Ren Tsai & Tin-Chih Toly Chen & Keng-Wei Hsu, 2021. "Energy-Efficient Production Planning Using a Two-Stage Fuzzy Approach," Mathematics, MDPI, vol. 9(10), pages 1-17, May.

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