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Constrained optimization in simulation : A novel approach

Author

Listed:
  • Kleijnen, Jack P.C.

    (Tilburg University, School of Economics and Management)

  • van Beers, W.C.M.

    (Tilburg University, School of Economics and Management)

  • van Nieuwenhuyse, I.

Abstract

This article presents a novel heuristic for constrained optimization of computationally expensive random simulation models. One output is selected as objective to be minimized, while other outputs must satisfy given threshold values. Moreover, the simulation inputs must be integer and satisfy linear or nonlinear constraints. The heuristic combines (i) sequentialized experimental designs to specify the simulation input combinations, (ii) Kriging (or Gaussian process or spatial correlation modeling) to analyze the global simulation input/output data resulting from these designs, and (iii) integer nonlinear programming to estimate the optimal solution from the Kriging metamodels. The heuristic is applied to an (s, S) inventory system and a call-center simulation, and compared with the popular commercial heuristic OptQuest embedded in the Arena versions 11 and 12. In these two applications the novel heuristic outperforms OptQuest in terms of number of simulated input combinations and quality of the estimated optimum.
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Suggested Citation

  • Kleijnen, Jack P.C. & van Beers, W.C.M. & van Nieuwenhuyse, I., 2010. "Constrained optimization in simulation : A novel approach," Other publications TiSEM b3655866-b593-4854-a4fd-5, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:b3655866-b593-4854-a4fd-559e6e6ff4a9
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    References listed on IDEAS

    as
    1. Kleijnen, Jack P.C. & Deflandre, David, 2006. "Validation of regression metamodels in simulation: Bootstrap approach," European Journal of Operational Research, Elsevier, vol. 170(1), pages 120-131, April.
    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. D den Hertog & J P C Kleijnen & A Y D Siem, 2006. "The correct Kriging variance estimated by bootstrapping," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(4), pages 400-409, April.
    4. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    5. van Beers, Wim C.M. & Kleijnen, Jack P.C., 2008. "Customized sequential designs for random simulation experiments: Kriging metamodeling and bootstrapping," European Journal of Operational Research, Elsevier, vol. 186(3), pages 1099-1113, May.
    6. Driessen, L. & Brekelmans, R.C.M. & Hamers, H.J.M. & den Hertog, D., 2001. "On D-Optimality Based Trust Regions for Black-Box Optimization Problems," Discussion Paper 2001-69, Tilburg University, Center for Economic Research.
    7. Sridhar Bashyam & Michael C. Fu, 1998. "Optimization of (s, S) Inventory Systems with Random Lead Times and a Service Level Constraint," Management Science, INFORMS, vol. 44(12-Part-2), pages 243-256, December.
    8. J P C Kleijnen & W C M van Beers, 2004. "Application-driven sequential designs for simulation experiments: Kriging metamodelling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(8), pages 876-883, August.
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    11. Sigrún Andradóttir, 2002. "Simulation Optimization: Integrating Research and Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 216-219, August.
    12. Júlíus Atlason & Marina A. Epelman & Shane G. Henderson, 2008. "Optimizing Call Center Staffing Using Simulation and Analytic Center Cutting-Plane Methods," Management Science, INFORMS, vol. 54(2), pages 295-309, February.
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    More about this item

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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