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Operations scheduling under electricity time-varying prices

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  • Yevgenia Mikhaylidi
  • Hussein Naseraldin
  • Liron Yedidsion

Abstract

Global climate change requires stakeholders to consider energy elements in their decision-making. Electricity costs, in particular, constitute a significant portion of operational costs in most manufacturing systems. The electricity bills can be lowered if electricity-consuming operations are correctly scheduled. We consider a manufacturing operations control problem with known time-varying electricity prices in a finite planning horizon. Each operation is unique and has its own concave electricity consumption function. Pre-emptions of operations are allowed, yet postponing an operation incurs a cumulative penalty for each time period. In addition, each pre-emption is considered a new operation. The electricity cost in each time period is exogenous and there exists a capacity constraint on the total electricity amount consumed in each period due to infrastructure and provider’s limitations. There is a fixed start-up cost incurred for switching on the machine and a fixed reservation cost incurred for keeping the machine ‘On’. The system also includes a rechargeable battery. The customer has to determine when to process each operation within the time horizon so as to minimise total electricity consumption and operations postponement penalty costs. A dynamic programming solution is proposed and the complexity of the models is analysed. After examining several special cases of the model, the optimum times to charge and discharge the rechargeable battery are determined. A polynomial time algorithm for a special case of a single operation with uniform capacity is proposed.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:23:p:7136-7157
    DOI: 10.1080/00207543.2015.1058981
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    References listed on IDEAS

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    Cited by:

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    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. Gert van Wyk & Vinessa Naidoo & E. Innocents Edoun, 2021. "Guiding Principles for Establishing Energy Consumption Reduction and Increase Production Performance in Manufacturing," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 502-515.
    4. Maroua Nouiri & Damien Trentesaux & Abdelghani Bekrar, 2019. "Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems," Energies, MDPI, vol. 12(23), pages 1-30, November.
    5. Markus Hilbert & Andreas Dellnitz & Andreas Kleine, 2023. "Production planning under RTP, TOU and PPA considering a redox flow battery storage system," Annals of Operations Research, Springer, vol. 328(2), pages 1409-1436, September.
    6. Gila E. Fruchter & Hussein Naseraldin, 2021. "Coordinating Carbon Emissions via Production Quantities: A Differential Game Approach," Games, MDPI, vol. 12(1), pages 1-16, February.
    7. 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.
    8. Songsong Chen & Feixiang Gong & Mingqiang Zhang & Jindou Yuan & Siyang Liao & Hongyin Chen & Dezhi Li & Shiming Tian & Xiaojian Hu, 2021. "Planning and Scheduling for Industrial Demand-Side Management: State of the Art, Opportunities and Challenges under Integration of Energy Internet and Industrial Internet," Sustainability, MDPI, vol. 13(14), pages 1-18, July.

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