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A New Energy-Aware Flexible Job Shop Scheduling Method Using Modified Biogeography-Based Optimization

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  • Hua Zhang
  • Ziwei Dai
  • Wenyu Zhang
  • Shuai Zhang
  • Yan Wang
  • Rongyu Liu

Abstract

Industry consumes approximately half of the total worldwide energy usage. With the increasingly rising energy costs in recent years, it is critically important to consider one of the most widely used energies, electricity, during the production planning process. We propose a new mathematical model that can determine efficient scheduling to minimize the makespan and electricity consumption cost (ECC) for the flexible job shop scheduling problem (FJSSP) under a time-of-use (TOU) policy. In addition to the traditional two subtasks in FJSSP, a new subtask called speed selection, which represents the selection of variable operating speeds, is added. Then, a modified biogeography-based optimization (MBBO) algorithm combined with variable neighborhood search (VNS) is proposed to solve the biobjective problem. Experiments are performed to verify the effectiveness of the proposed MBBO algorithm for obtaining an improved scheduling solution compared to the basic biogeography-based optimization (BBO) algorithm, genetic algorithm (GA), and harmony search (HS).

Suggested Citation

  • Hua Zhang & Ziwei Dai & Wenyu Zhang & Shuai Zhang & Yan Wang & Rongyu Liu, 2017. "A New Energy-Aware Flexible Job Shop Scheduling Method Using Modified Biogeography-Based Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-12, August.
    • Handle: RePEc:hin:jnlmpe:7249876
    DOI: 10.1155/2017/7249876
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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. Shen, Liji & Dauzère-Pérès, Stéphane & Maecker, Söhnke, 2023. "Energy cost efficient scheduling in flexible job-shop manufacturing systems," European Journal of Operational Research, Elsevier, vol. 310(3), pages 992-1016.
    3. 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).
    4. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2021. "Job Scheduling under Time-of-Use Energy Tariffs for Sustainable Manufacturing: A Survey," LIDAM Discussion Papers CORE 2021019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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