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Optimization for simulation: LAD accelerator

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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.

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Authors and Affiliations

  1. George Washington University, Washington, DC, USA

    Miguel A. Lejeune

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    François Margot

Authors
  1. Miguel A. Lejeune
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  2. François Margot
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Correspondence to Miguel A. Lejeune.

Additional information

François Margot was supported by ONR grant N00014-97-1-0196.

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Lejeune, M.A., Margot, F. Optimization for simulation: LAD accelerator. Ann Oper Res 188, 285–305 (2011). https://doi.org/10.1007/s10479-009-0518-3

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