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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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    1. P. Hammer & A. Kogan & M. Lejeune, 2011. "Reverse-engineering country risk ratings: a combinatorial non-recursive model," Annals of Operations Research, Springer, vol. 188(1), pages 185-213, August.
    2. Michael C. Fu, 2002. "Feature Article: Optimization for simulation: Theory vs. Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 192-215, August.
    3. Peter Hammer & Tibérius Bonates, 2006. "Logical analysis of data—An overview: From combinatorial optimization to medical applications," Annals of Operations Research, Springer, vol. 148(1), pages 203-225, November.
    4. Bernd Engelmann & Robert Rauhmeier (ed.), 2006. "The Basel II Risk Parameters," Springer Books, Springer, number 978-3-540-33087-5, June.
    5. L. Jeff Hong & Barry L. Nelson, 2006. "Discrete Optimization via Simulation Using COMPASS," Operations Research, INFORMS, vol. 54(1), pages 115-129, February.
    6. Stephen E. Chick & Koichiro Inoue, 2001. "New Two-Stage and Sequential Procedures for Selecting the Best Simulated System," Operations Research, INFORMS, vol. 49(5), pages 732-743, October.
    7. Belarmino Adenso-Díaz & Manuel Laguna, 2006. "Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search," Operations Research, INFORMS, vol. 54(1), pages 99-114, February.
    8. James P. Kelly, 2002. "Simulation Optimization is Evolving," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 223-225, August.
    9. Y-C Ho & C G Cassandras & C-H Chen & L Dai, 2000. "Ordinal optimisation and simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(4), pages 490-500, April.
    10. Barry L. Nelson & Julie Swann & David Goldsman & Wheyming Song, 2001. "Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large," Operations Research, INFORMS, vol. 49(6), pages 950-963, December.
    11. Justin Boesel & Barry L. Nelson & Seong-Hee Kim, 2003. "Using Ranking and Selection to “Clean Up” after Simulation Optimization," Operations Research, INFORMS, vol. 51(5), pages 814-825, October.
    12. Benjamin W. Lin & Ronald L. Rardin, 1979. "Controlled Experimental Design for Statistical Comparison of Integer Programming Algorithms," Management Science, INFORMS, vol. 25(12), pages 1258-1271, December.
    13. Hammer, P.L. & Kogan, A. & Lejeune, M.A., 2006. "Modeling country risk ratings using partial orders," European Journal of Operational Research, Elsevier, vol. 175(2), pages 836-859, December.
    14. Michael C. Fu, 2002. "Simulation Optimization in the Future: Evolution or Revolution?," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 226-227, August.
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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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