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Economic batch sizing and scheduling on parallel machines under time-of-use electricity pricing

Author

Listed:
  • Mao Tan

    (Xiangtan University)

  • Bin Duan

    (Xiangtan University)

  • Yongxin Su

    (Xiangtan University)

Abstract

Time-of-use (TOU) electricity pricing provides new opportunity for power-intensive users to reduce their electricity costs. In order to achieve optimal economic scheduling under TOU pricing for batch production, there are difficulties existed because batch production loads are not fixed or directly adjustable but closely related with time and machine dependant production schedule. Provided that production capacity is abundant and minimizing makespan is not the primary target, a MILP model that integrates batch sizing and scheduling on parallel machines is proposed, in which the objective is to minimize electricity costs in production by utilizing TOU pricing. Use cases are provided to assess the proposed model, experimental results show that the proposed model reduce electricity costs significantly, and promote peak load regulation of power grid.

Suggested Citation

  • Mao Tan & Bin Duan & Yongxin Su, 2018. "Economic batch sizing and scheduling on parallel machines under time-of-use electricity pricing," Operational Research, Springer, vol. 18(1), pages 105-122, April.
    • Handle: RePEc:spr:operea:v:18:y:2018:i:1:d:10.1007_s12351-016-0256-7
    DOI: 10.1007/s12351-016-0256-7
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    References listed on IDEAS

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    1. Sifaleras, Angelo & Konstantaras, Ioannis & Mladenović, Nenad, 2015. "Variable neighborhood search for the economic lot sizing problem with product returns and recovery," International Journal of Production Economics, Elsevier, vol. 160(C), pages 133-143.
    2. Ashok, S., 2006. "Peak-load management in steel plants," Applied Energy, Elsevier, vol. 83(5), pages 413-424, May.
    3. Archetti, Claudia & Bertazzi, Luca & Grazia Speranza, M., 2014. "Polynomial cases of the economic lot sizing problem with cost discounts," European Journal of Operational Research, Elsevier, vol. 237(2), pages 519-527.
    4. Yevgenia Mikhaylidi & Hussein Naseraldin & Liron Yedidsion, 2015. "Operations scheduling under electricity time-varying prices," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7136-7157, December.
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    Cited by:

    1. Peng Wu & Junheng Cheng & Feng Chu, 2021. "Large-scale energy-conscious bi-objective single-machine batch scheduling under time-of-use electricity tariffs via effective iterative heuristics," Annals of Operations Research, Springer, vol. 296(1), pages 471-494, January.
    2. Xiangxin An & Guojin Si & Tangbin Xia & Qinming Liu & Yaping Li & Rui Miao, 2022. "Operation and Maintenance Optimization for Manufacturing Systems with Energy Management," Energies, MDPI, vol. 15(19), pages 1-19, October.
    3. 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).
    4. Shima Javanmard & Behrouz Afshar-Nadjafi & Seyed Taghi Akhavan Niaki, 2022. "A bi-objective model for scheduling of multiple projects under multi-skilled workforce for distributed load energy usage," Operational Research, Springer, vol. 22(3), pages 2245-2280, July.

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