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Simulation optimization for the stochastic economic lot scheduling problem

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  • Nils Löhndorf
  • Stefan Minner

Abstract

This article studies simulation optimization methods for the stochastic economic lot scheduling problem. In contrast with prior research, the focus of this work is on methods that treat this problem as a black box. Based on a large-scale numerical study, approximate dynamic programming is compared with a global search for parameters of simple control policies. Two value function approximation schemes are proposed that are based on linear combinations of piecewise-constant functions as well as control policies that can be described by a small set of parameters. While approximate value iteration worked well for small problems with three products, it was clearly outperformed by the global policy search as soon as problem size increased. The most reliable choice in this study was a globally optimized fixed-cycle policy. An additional analysis of the response surface of model parameters on optimal average cost revealed that the cost effect of product diversity was negligible.

Suggested Citation

  • Nils Löhndorf & Stefan Minner, 2013. "Simulation optimization for the stochastic economic lot scheduling problem," IISE Transactions, Taylor & Francis Journals, vol. 45(7), pages 796-810.
    • Handle: RePEc:taf:uiiexx:v:45:y:2013:i:7:p:796-810
    DOI: 10.1080/0740817X.2012.662310
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    Cited by:

    1. Löhndorf, Nils & Riel, Manuel & Minner, Stefan, 2014. "Simulation optimization for the stochastic economic lot scheduling problem with sequence-dependent setup times," International Journal of Production Economics, Elsevier, vol. 157(C), pages 170-176.
    2. Briskorn, Dirk & Zeise, Philipp & Packowski, Josef, 2016. "Quasi-fixed cyclic production schemes for multiple products with stochastic demand," European Journal of Operational Research, Elsevier, vol. 252(1), pages 156-169.
    3. Tamiti Kenza & Ourbih-Tari Megdouda & Aloui Abdelouhab & Idjis Khelidja, 2018. "The use of variance reduction, relative error and bias in testing the performance of M/G/1 retrial queues estimators in Monte Carlo simulation," Monte Carlo Methods and Applications, De Gruyter, vol. 24(3), pages 165-178, September.
    4. Hossein Jahandideh & Kumar Rajaram & Kevin McCardle, 2020. "Production Campaign Planning Under Learning and Decay," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 615-632, May.

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