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Optimization for simulation: LAD accelerator

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  • Miguel Lejeune
  • François Margot

Abstract

The goal of this paper is to address the problem of evaluating the performance of a system running under unknown values for its stochastic parameters. A new approach called LAD for Simulation, based on simulation and classification software, is presented. It uses a number of simulations with very few replications and records the mean value of directly measurable quantities (called observables). These observables are used as input to a classification model that produces a prediction for the performance of the system. Application to an assemble-to-order system from the literature is described and detailed results illustrate the strength of the method. Copyright Springer Science+Business Media, LLC 2011

Suggested Citation

  • Miguel Lejeune & François Margot, 2011. "Optimization for simulation: LAD accelerator," Annals of Operations Research, Springer, vol. 188(1), pages 285-305, August.
    • Handle: RePEc:spr:annopr:v:188:y:2011:i:1:p:285-305:10.1007/s10479-009-0518-3
    DOI: 10.1007/s10479-009-0518-3
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    References listed on IDEAS

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    Cited by:

    1. Lejeune, Miguel & Lozin, Vadim & Lozina, Irina & Ragab, Ahmed & Yacout, Soumaya, 2019. "Recent advances in the theory and practice of Logical Analysis of Data," European Journal of Operational Research, Elsevier, vol. 275(1), pages 1-15.
    2. Bagchi, Prabir & Lejeune, Miguel A. & Alam, A., 2014. "How supply competency affects FDI decisions: Some insights," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 239-251.
    3. Travaughn C. Bain & Juan F. Avila-Herrera & Ersoy Subasi & Munevver Mine Subasi, 2020. "Logical analysis of multiclass data with relaxed patterns," Annals of Operations Research, Springer, vol. 287(1), pages 11-35, April.

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