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Mathematical programming models for joint simulation–optimization applied to closed queueing networks

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  • Arianna Alfieri
  • Andrea Matta
  • Giulia Pedrielli

Abstract

The optimization of stochastic Discrete Event Systems (DESs) is a critical and difficult task. The search for the optimal system configuration (optimization problem) requires the assessment of the system performance (simulation problem), resulting in a simulation–optimization problem. In the past ten years, a noticeable research effort has been devoted to this area. Recently, mathematical programming has been proposed to integrate simulation and optimization for multi-stage open queueing networks. This paper proposes the application of this approach to closed queueing networks. In particular, the optimal pallet allocation problem is tackled through linear mathematical programming models for simulation–optimization. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • 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.
  • Handle: RePEc:spr:annopr:v:231:y:2015:i:1:p:105-127:10.1007/s10479-013-1480-7
    DOI: 10.1007/s10479-013-1480-7
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    References listed on IDEAS

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    1. 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.
    2. Abdellatif Bouhchouch & Yannick Frein & Yves Dallery, 1993. "Analysis of a closed‐loop manufacturing system with finite buffers," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 9(2), pages 111-125, June.
    3. L. Jeff Hong & Barry L. Nelson, 2006. "Discrete Optimization via Simulation Using COMPASS," Operations Research, INFORMS, vol. 54(1), pages 115-129, February.
    4. N. Maggio & A. Matta & S. Gershwin & T. Tolio, 2009. "A decomposition approximation for three-machine closed-loop production systems with unreliable machines, finite buffers and a fixed population," IISE Transactions, Taylor & Francis Journals, vol. 41(6), pages 562-574.
    5. Wai Kin (Victor) Chan & Lee Schruben, 2008. "Optimization Models of Discrete-Event System Dynamics," Operations Research, INFORMS, vol. 56(5), pages 1218-1237, October.
    6. Stephen M. Robinson, 1996. "Analysis of Sample-Path Optimization," Mathematics of Operations Research, INFORMS, vol. 21(3), pages 513-528, August.
    7. Justin Boesel & Barry L. Nelson & Seong-Hee Kim, 2003. "Using Ranking and Selection to “Clean Up” after Simulation Optimization," Operations Research, INFORMS, vol. 51(5), pages 814-825, October.
    8. Jack P.C. Kleijnen, 2015. "Design and Analysis of Simulation Experiments," International Series in Operations Research and Management Science, Springer, edition 2, number 978-3-319-18087-8, December.
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    Cited by:

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