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Robust Scheduling to Hedge Against Processing Time Uncertainty in Single-Stage Production

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Author Info

  • Richard L. Daniels

    (School of Management, Georgia Institute of Technology, Atlanta, Georgia 30332-0520)

  • Panagiotis Kouvelis

    (Fuqua School of Business, Duke University, Durham, North Carolina 27706)

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    Abstract

    Schedulers confronted with significant processing time uncertainty often discover that a schedule which is optimal with respect to a deterministic or stochastic scheduling model yields quite poor performance when evaluated relative to the actual processing times. In these environments, the notion of schedule robustness, i.e., determining the schedule with the best worst-case performance compared to the corresponding optimal solution over all potential realizations of job processing times, is a more appropriate guide to schedule selection. In this paper, we formalize the robust scheduling concept for scheduling situations with uncertain or variable processing times. To illustrate the development of solution approaches for a robust scheduling problem, we consider a single-machine environment where the performance criterion of interest is the total flow time over all jobs. We define two measures of schedule robustness, formulate the robust scheduling problem, establish its complexity, describe properties of the optimal schedule, and present exact and heuristic solution procedures. Extensive computational results are reported to demonstrate the efficiency and effectiveness of the proposed solution procedures.

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    File URL: http://dx.doi.org/10.1287/mnsc.41.2.363
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 41 (1995)
    Issue (Month): 2 (February)
    Pages: 363-376

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    Handle: RePEc:inm:ormnsc:v:41:y:1995:i:2:p:363-376

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    Related research

    Keywords: production scheduling; processing time uncertainty; worst-case analysis;

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    Cited by:
    1. Cowling, Peter & Johansson, Marcus, 2002. "Using real time information for effective dynamic scheduling," European Journal of Operational Research, Elsevier, vol. 139(2), pages 230-244, June.
    2. Kimms, A, 1998. "Stability Measures for Rolling Schedules with Applications to Capacity Expansion Planning, Master Production Scheduling, and Lot Sizing," Omega, Elsevier, vol. 26(3), pages 355-366, June.
    3. Kasperski, Adam & Kurpisz, Adam & Zieliński, Paweł, 2012. "Approximating a two-machine flow shop scheduling under discrete scenario uncertainty," European Journal of Operational Research, Elsevier, vol. 217(1), pages 36-43.
    4. Al-Hinai, Nasr & ElMekkawy, T.Y., 2011. "Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm," International Journal of Production Economics, Elsevier, vol. 132(2), pages 279-291, August.
    5. Mausser, Helmut E. & Laguna, Manuel, 1999. "A heuristic to minimax absolute regret for linear programs with interval objective function coefficients," European Journal of Operational Research, Elsevier, vol. 117(1), pages 157-174, August.
    6. Aytug, Haldun & Lawley, Mark A. & McKay, Kenneth & Mohan, Shantha & Uzsoy, Reha, 2005. "Executing production schedules in the face of uncertainties: A review and some future directions," European Journal of Operational Research, Elsevier, vol. 161(1), pages 86-110, February.
    7. Xiong, Jian & Xing, Li-ning & Chen, Ying-wu, 2013. "Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns," International Journal of Production Economics, Elsevier, vol. 141(1), pages 112-126.
    8. Conde, Eduardo, 2012. "On a constant factor approximation for minmax regret problems using a symmetry point scenario," European Journal of Operational Research, Elsevier, vol. 219(2), pages 452-457.
    9. Şeker, Merve & Noyan, Nilay, 2012. "Stochastic optimization models for the airport gate assignment problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(2), pages 438-459.
    10. Ng, Tsan Sheng, 2013. "Robust regret for uncertain linear programs with application to co-production models," European Journal of Operational Research, Elsevier, vol. 227(3), pages 483-493.
    11. Cao, Qidong & Patterson, J. Wayne & Bai, Xue, 2005. "Reexamination of processing time uncertainty," European Journal of Operational Research, Elsevier, vol. 164(1), pages 185-194, July.
    12. Lin, Jun & Ng, Tsan Sheng, 2011. "Robust multi-market newsvendor models with interval demand data," European Journal of Operational Research, Elsevier, vol. 212(2), pages 361-373, July.
    13. Wang, Juite, 2004. "A fuzzy robust scheduling approach for product development projects," European Journal of Operational Research, Elsevier, vol. 152(1), pages 180-194, January.
    14. Herroelen, Willy & Leus, Roel, 2005. "Project scheduling under uncertainty: Survey and research potentials," European Journal of Operational Research, Elsevier, vol. 165(2), pages 289-306, September.

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