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Scheduling jobs sharing multiple resources under uncertainty: A stochastic programming approach

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  • Brian Keller
  • GÜzİn Bayraksan

Abstract

A two-stage stochastic integer program to determine an optimal schedule for jobs requiring multiple classes of resources under uncertain processing times, due dates, resource consumption and availabilities is formulated. Temporary resource capacity expansion for a penalty is allowed. Potential applications of this model include team scheduling problems that arise in service industries such as engineering consulting and operating room scheduling. An exact solution method is developed based on Benders decomposition for problems with a moderate number of scenarios. Benders decomposition is then embedded within a sampling-based solution method for problems with a large number of scenarios. A sequential sampling procedure is modified to allow for approximate solution of integer programs and its asymptotic validity and finite stopping are proved under this modification. The solution methodologies are compared on a set of test problems. Several algorithmic enhancements are added to improve efficiency.

Suggested Citation

  • Brian Keller & GÜzİn Bayraksan, 2010. "Scheduling jobs sharing multiple resources under uncertainty: A stochastic programming approach," IISE Transactions, Taylor & Francis Journals, vol. 42(1), pages 16-30.
    • Handle: RePEc:taf:uiiexx:v:42:y:2010:i:1:p:16-30
    DOI: 10.1080/07408170902942683
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    Citations

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    Cited by:

    1. Li, Haitao & Womer, Norman K., 2015. "Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 246(1), pages 20-33.
    2. Zhen, Lu, 2015. "Tactical berth allocation under uncertainty," European Journal of Operational Research, Elsevier, vol. 247(3), pages 928-944.
    3. Arezoo Atighehchian & Mohammad Mehdi Sepehri & Pejman Shadpour & Kamran Kianfar, 2020. "A two-step stochastic approach for operating rooms scheduling in multi-resource environment," Annals of Operations Research, Springer, vol. 292(1), pages 191-214, September.
    4. Jangho Park & Rebecca Stockbridge & Güzin Bayraksan, 2021. "Variance reduction for sequential sampling in stochastic programming," Annals of Operations Research, Springer, vol. 300(1), pages 171-204, May.
    5. Rebecca Stockbridge & Güzin Bayraksan, 2016. "Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming," Computational Optimization and Applications, Springer, vol. 64(2), pages 407-431, June.

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