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Mathematical programming formulations for single-machine scheduling problems while considering renewable energy uncertainty

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  • Cheng-Hsiang Liu

Abstract

Carbon dioxide (CO 2 ) in particular is by far the primary driver of global warming. One of the most effective ways to reduce CO 2 emissions is to increase the amount of power from renewable energy. A key challenge in utilising renewable energies, such as wind and solar, is their uncertainty in terms of when and to what degree and force renewable energies will become available next time. This study uses interval number theory for renewable energy in uncertainty modelling and proposes two novel interval single-machine scheduling problems, and . A solution procedure is formulated to optimise these problems with interval numbers using interval arithmetic. Additionally, this study derives Pareto-optimal solutions of the bi-objective optimisation problem, , using the lexicographic-weighted Tchebycheff method. Some managerial implications are obtained by parameter analysis. Analytical results offer decision-makers an intuitive view of how these factors impact scheduling results and provide practical guidelines for real-life production.

Suggested Citation

  • Cheng-Hsiang Liu, 2016. "Mathematical programming formulations for single-machine scheduling problems while considering renewable energy uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 1122-1133, February.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:4:p:1122-1133
    DOI: 10.1080/00207543.2015.1048380
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    Cited by:

    1. 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.
    2. 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.
    3. Trevino-Martinez, Samuel & Sawhney, Rapinder & Shylo, Oleg, 2022. "Energy-carbon footprint optimization in sequence-dependent production scheduling," Applied Energy, Elsevier, vol. 315(C).
    4. Weiwei Cui & Lin Li & Zhiqiang Lu, 2019. "Energy‐efficient scheduling for sustainable manufacturing systems with renewable energy resources," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(2), pages 154-173, March.
    5. Ursavas, Evrim, 2017. "A benders decomposition approach for solving the offshore wind farm installation planning at the North Sea," European Journal of Operational Research, Elsevier, vol. 258(2), pages 703-714.
    6. Bohlayer, Markus & Fleschutz, Markus & Braun, Marco & Zöttl, Gregor, 2020. "Energy-intense production-inventory planning with participation in sequential energy markets," Applied Energy, Elsevier, vol. 258(C).
    7. Elisa Negri & Vibhor Pandhare & Laura Cattaneo & Jaskaran Singh & Marco Macchi & Jay Lee, 2021. "Field-synchronized Digital Twin framework for production scheduling with uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1207-1228, April.

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