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Mathematical programming formulations for approximate simulation of multistage production systems

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  • Alfieri, Arianna
  • Matta, Andrea

Abstract

Mathematical programming representation has been recently used to describe the behavior of discrete event systems as well as their formal properties. This new way of representing discrete event systems paves the way to the creation of simpler mathematical programming models that reduce the complexity of the system analysis. The paper proposes an approximate representation for a class of production systems characterized by several stages, limited buffer capacities and stochastic production times. The approximation exploits the concept of a time buffer, modeled as a constraint that put into a temporal relationship the completion times of two customers in a sample path. The main advantage of the proposed formulation is that it preserves its linearity even when used for optimization and, for such a reason, it can be adopted in simulation–optimization problems to reduce the initial solution space. The approximate formulation is applied to relevant problems such as buffer capacity allocation in manufacturing systems and control parameters setting in pull systems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:219:y:2012:i:3:p:773-783
    DOI: 10.1016/j.ejor.2011.12.044
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    References listed on IDEAS

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    1. Lutz, Christian M. & Roscoe Davis, K. & Sun, Minghe, 1998. "Determining buffer location and size in production lines using tabu search," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 301-316, April.
    2. Wai Kin (Victor) Chan & Lee Schruben, 2008. "Optimization Models of Discrete-Event System Dynamics," Operations Research, INFORMS, vol. 56(5), pages 1218-1237, October.
    3. Stephen M. Robinson, 1996. "Analysis of Sample-Path Optimization," Mathematics of Operations Research, INFORMS, vol. 21(3), pages 513-528, August.
    4. 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, April.
    5. Jensen, Paul A. & Pakath, Ramakrishnan & Wilson, James R., 1991. "Optimal buffer inventories for multistage production systems with failures," European Journal of Operational Research, Elsevier, vol. 51(3), pages 313-326, April.
    6. L. Jeff Hong & Barry L. Nelson, 2006. "Discrete Optimization via Simulation Using COMPASS," Operations Research, INFORMS, vol. 54(1), pages 115-129, February.
    7. Stanley Gershwin & James Schor, 2000. "Efficient algorithms for buffer space allocation," Annals of Operations Research, Springer, vol. 93(1), pages 117-144, January.
    8. Jean-Luc Deleersnyder & Thom J. Hodgson & Henri Muller-Malek & Peter J. O'Grady, 1989. "Kanban Controlled Pull Systems: An Analytic Approach," Management Science, INFORMS, vol. 35(9), pages 1079-1091, September.
    9. 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.
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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. Wai Kin Victor Chan, 2016. "Linear Programming Formulation of Idle Times for Single-Server Discrete-Event Simulation Models," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(05), pages 1-17, October.
    4. Khayyati, Siamak & Tan, Barış, 2020. "Data-driven control of a production system by using marking-dependent threshold policy," International Journal of Production Economics, Elsevier, vol. 226(C).
    5. Kolb, Oliver & Göttlich, Simone, 2015. "A continuous buffer allocation model using stochastic processes," European Journal of Operational Research, Elsevier, vol. 242(3), pages 865-874.

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