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Optimization Models of Discrete-Event System Dynamics

Author

Listed:
  • Wai Kin (Victor) Chan

    (Department of Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, Troy, New York 12180)

  • Lee Schruben

    (Department of Industrial Engineering and Operations Research, University of California, Berkeley, California 94720)

Abstract

A methodology is given for modeling the dynamics of discrete-event stochastic systems as optimization problems. The intent is to provide a means to utilize the rich mathematical theory and algorithms of optimization in the study of this important class of systems. A procedure for mapping a simulation event relationship graph into a mixed-integer program is presented, along with examples of queueing networks and manufacturing systems that illustrate the approach. Several potential applications are examined, including automatic constraint generation for optimal resource scheduling, representations of max-plus algebra models for queueing system dynamics, response gradient estimation, and an unconventional technique for simulating queueing systems using virtual resources that are identified from the optimization models for these systems.

Suggested Citation

  • Wai Kin (Victor) Chan & Lee Schruben, 2008. "Optimization Models of Discrete-Event System Dynamics," Operations Research, INFORMS, vol. 56(5), pages 1218-1237, October.
    • Handle: RePEc:inm:oropre:v:56:y:2008:i:5:p:1218-1237
    DOI: 10.1287/opre.1080.0559
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    References listed on IDEAS

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    Cited by:

    1. Arianna Alfieri & Andrea Matta & Giulia Pedrielli, 2015. "Mathematical programming models for joint simulation–optimization applied to closed queueing networks," Annals of Operations Research, Springer, vol. 231(1), pages 105-127, August.
    2. Alfieri, Arianna & Matta, Andrea, 2013. "Mathematical programming time-based decomposition algorithm for discrete event simulation," European Journal of Operational Research, Elsevier, vol. 231(3), pages 557-566.
    3. George Liberopoulos, 2020. "Comparison of optimal buffer allocation in flow lines under installation buffer, echelon buffer, and CONWIP policies," Flexible Services and Manufacturing Journal, Springer, vol. 32(2), pages 297-365, June.
    4. S. Göttlich & S. Kühn & J. A. Schwarz & R. Stolletz, 2016. "Approximations of time-dependent unreliable flow lines with finite buffers," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 83(3), pages 295-323, June.
    5. Alfieri, Arianna & Matta, Andrea, 2012. "Mathematical programming formulations for approximate simulation of multistage production systems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 773-783.
    6. Alexander H. Gose & Brian T. Denton, 2016. "Sequential Bounding Methods for Two-Stage Stochastic Programs," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 351-369, May.

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