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Single machine scheduling with common assignable due date/due window to minimize total weighted early and late work

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  • Shabtay, Dvir
  • Mosheiov, Gur
  • Oron, Daniel

Abstract

Traditional scheduling models assume that due dates are predefined and the aim is to find a schedule that minimizes a given scheduling criterion with respect to the given set of due dates. A more recent trend consists of models that focus on coordinating two sets of decisions: due date assignment to customers and determining a job schedule. We follow this trend by analyzing a single machine scheduling problem, where the scheduler is tasked with assigning a common due date to all jobs in order to minimize an objective function that includes job-dependent penalties due to early and late work. We show that the problem is solvable in linear time if the common due date value is unbounded, and in O(nlogn) time if it is bounded from above. We then extend the analysis to the case where a common due window has to be assigned to all jobs. We show that when the location of the due window is unbounded, the problem is solvable in O(nlogn) time (and further in linear time if the length of the due window is unbounded as well). However, it becomes NP-hard when it is bounded. We complement our analysis by (i) providing a pseudo-polynomial time algorithm to solve this hard variant of the problem, and (ii) study two special cases of this hard variant that are solvable in polynomial time.

Suggested Citation

  • Shabtay, Dvir & Mosheiov, Gur & Oron, Daniel, 2022. "Single machine scheduling with common assignable due date/due window to minimize total weighted early and late work," European Journal of Operational Research, Elsevier, vol. 303(1), pages 66-77.
  • Handle: RePEc:eee:ejores:v:303:y:2022:i:1:p:66-77
    DOI: 10.1016/j.ejor.2022.02.017
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    References listed on IDEAS

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    Cited by:

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    2. Oğuzhan Ahmet Arik, 2023. "A heuristic for single machine common due date assignment problem with different earliness/tardiness weights," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1561-1574, September.
    3. Ming-Hui Li & Dan-Yang Lv & Yuan-Yuan Lu & Ji-Bo Wang, 2024. "Scheduling with Group Technology, Resource Allocation, and Learning Effect Simultaneously," Mathematics, MDPI, vol. 12(7), pages 1-21, March.
    4. Justkowiak, Jan-Erik & Kovalev, Sergey & Kovalyov, Mikhail Y. & Pesch, Erwin, 2023. "Single machine scheduling with assignable due dates to minimize maximum and total late work," European Journal of Operational Research, Elsevier, vol. 308(1), pages 76-83.
    5. Yi-Chun Wang & Si-Han Wang & Ji-Bo Wang, 2023. "Resource Allocation Scheduling with Position-Dependent Weights and Generalized Earliness–Tardiness Cost," Mathematics, MDPI, vol. 11(1), pages 1-11, January.
    6. Li-Han Zhang & Dan-Yang Lv & Ji-Bo Wang, 2023. "Two-Agent Slack Due-Date Assignment Scheduling with Resource Allocations and Deteriorating Jobs," Mathematics, MDPI, vol. 11(12), pages 1-12, June.
    7. Lu, Haimin & Pei, Zhi, 2023. "Single machine scheduling with release dates: A distributionally robust approach," European Journal of Operational Research, Elsevier, vol. 308(1), pages 19-37.

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