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Optimizing stochastic production-inventory systems: A heuristic based on simulation and regression analysis

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  • Arreola-Risa, Antonio
  • Giménez-García, Víctor M.
  • Martínez-Parra, José Luis

Abstract

We present a heuristic optimization method for stochastic production-inventory systems that defy analytical modelling and optimization. The proposed heuristic takes advantage of simulation while at the same time minimizes the impact of the dimensionality curse by using regression analysis. The heuristic was developed and tested for an oil and gas company, which decided to adopt the heuristic as the optimization method for a supply-chain design project. To explore the performance of the heuristic in general settings, we conducted a simulation experiment on 900 test problems. We found that the average cost error of using the proposed heuristic was reasonably low for practical applications.

Suggested Citation

  • Arreola-Risa, Antonio & Giménez-García, Víctor M. & Martínez-Parra, José Luis, 2011. "Optimizing stochastic production-inventory systems: A heuristic based on simulation and regression analysis," European Journal of Operational Research, Elsevier, vol. 213(1), pages 107-118, August.
    • Handle: RePEc:eee:ejores:v:213:y:2011:i:1:p:107-118
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    References listed on IDEAS

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    Cited by:

    1. Tsai, Shing Chih & Fu, Sheng Yang, 2014. "Genetic-algorithm-based simulation optimization considering a single stochastic constraint," European Journal of Operational Research, Elsevier, vol. 236(1), pages 113-125.
    2. H. G. H. Tiemessen & M. Fleischmann & G. J. Houtum, 2017. "Dynamic control in multi-item production/inventory systems," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(1), pages 165-191, January.
    3. Ivanov, Dmitry & Sokolov, Boris, 2013. "Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty," European Journal of Operational Research, Elsevier, vol. 224(2), pages 313-323.

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