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Scheduling with variable time slot costs

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  • Guohua Wan
  • Xiangtong Qi

Abstract

In this article, we study a class of new scheduling models where time slot costs have to be taken into consideration. In such models, processing a job will incur certain cost which is determined by the time slots occupied by the job in a schedule. The models apply when operational costs vary over time. The objective of the scheduling models is to minimize the total time slot costs plus a traditional scheduling performance measure. We consider the following performance measures: total completion time, maximum lateness/tardiness, total weighted number of tardy jobs, and total tardiness. We prove the intractability of the models under general parameters and provide polynomial‐time algorithms for special cases with non‐increasing time slot costs.© 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010

Suggested Citation

  • Guohua Wan & Xiangtong Qi, 2010. "Scheduling with variable time slot costs," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(2), pages 159-171, March.
    • Handle: RePEc:wly:navres:v:57:y:2010:i:2:p:159-171
    DOI: 10.1002/nav.20393
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    References listed on IDEAS

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    Cited by:

    1. Kan Fang & Nelson Uhan & Fu Zhao & John Sutherland, 2016. "Scheduling on a single machine under time-of-use electricity tariffs," Annals of Operations Research, Springer, vol. 238(1), pages 199-227, March.
    2. Lin Chen & Nicole Megow & Roman Rischke & Leen Stougie & José Verschae, 2021. "Optimal algorithms for scheduling under time-of-use tariffs," Annals of Operations Research, Springer, vol. 304(1), pages 85-107, September.
    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. 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.
    5. Chen, Bo & Zhang, Xiandong, 2019. "Scheduling with time-of-use costs," European Journal of Operational Research, Elsevier, vol. 274(3), pages 900-908.
    6. Trevino-Martinez, Samuel & Sawhney, Rapinder & Shylo, Oleg, 2022. "Energy-carbon footprint optimization in sequence-dependent production scheduling," Applied Energy, Elsevier, vol. 315(C).
    7. 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.
    8. Briskorn, Dirk & Davari, Morteza & Matuschke, Jannik, 2021. "Single-machine scheduling with an external resource," European Journal of Operational Research, Elsevier, vol. 293(2), pages 457-468.
    9. Farnaz Ghazi Nezami & Mojtaba Heydar, 2019. "Energy-aware Economic Production Quantity model with variable energy pricing," Operational Research, Springer, vol. 19(1), pages 201-218, March.
    10. 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.
    11. Markó Horváth & Tamás Kis, 2020. "Polyhedral results for position-based scheduling of chains on a single machine," Annals of Operations Research, Springer, vol. 284(1), pages 283-322, January.
    12. Michal Penn & Tal Raviv, 2021. "Complexity and algorithms for min cost and max profit scheduling under time-of-use electricity tariffs," Journal of Scheduling, Springer, vol. 24(1), pages 83-102, February.

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