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Production Scheduling Theory: Just Where Is It Applicable?

Author

Listed:
  • Victor Portougal

    (Department of Management Science and Information Systems, The University of Auckland, Private Bag 92019, Auckland, New Zealand)

  • David J. Robb

    (Department of Management Science and Information Systems, The University of Auckland)

Abstract

A proliferation of scheduling research has done little to improve production planning practice, despite calls for more comprehensive models. Using a four-factor classification of planning environments (planning level, production type, production strategy, and production cycle time) we show scheduling theory is relevant in few settings. For example, in increasingly common short-cycle environments, where production cycle times are shorter than the planning period, the order in which one processes jobs is seldom important. Moreover, even in long-cycle environments, capacity is seldom fixed, with managers often negotiating for enough capacity to make scheduling fairly easy. Based on extensive consulting experience in Australasia, we call for caution in applying scheduling theory. While complex models are pertinent in some cases, more benefit often arises from establishing appropriate performance measures, planning periods, capacity negotiation processes, and uncertainty reduction measures.

Suggested Citation

  • Victor Portougal & David J. Robb, 2000. "Production Scheduling Theory: Just Where Is It Applicable?," Interfaces, INFORMS, vol. 30(6), pages 64-76, December.
  • Handle: RePEc:inm:orinte:v:30:y:2000:i:6:p:64-76
    DOI: 10.1287/inte.30.6.64.11623
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    References listed on IDEAS

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    Cited by:

    1. Khalil Tliba & Thierno M. L. Diallo & Olivia Penas & Romdhane Ben Khalifa & Noureddine Ben Yahia & Jean-Yves Choley, 2023. "Digital twin-driven dynamic scheduling of a hybrid flow shop," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2281-2306, June.
    2. Seokcheol Chang & Jaewoo Chung, 2013. "Optimization Models for Production Planning in LG Display," Interfaces, INFORMS, vol. 43(6), pages 518-529, December.
    3. Jiawen Hu & Zuhua Jiang & Hong Wang, 2017. "Joint Optimization of Production Plan and Preventive Maintenance Schedule by Stackelberg Game," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(04), pages 1-28, August.
    4. Agnetis, Alessandro & Bianciardi, Caterina & Iasparra, Nicola, 2019. "Integrating lean thinking and mathematical optimization: A case study in appointment scheduling of hematological treatments," Operations Research Perspectives, Elsevier, vol. 6(C).
    5. Singer, Marcos & Donoso, Patricio, 2008. "Empirical validation of an activity-based optimization system," International Journal of Production Economics, Elsevier, vol. 113(1), pages 335-345, May.

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