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Application-driven Sequential Designs for Simulation Experiments : Kriging Metamodeling

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  • Kleijnen, J.P.C.

    (Tilburg University, School of Economics and Management)

  • van Beers, W.C.M.

    (Tilburg University, School of Economics and Management)

Abstract

This paper proposes a novel method to select an experimental design for interpolation in simulation. Although the paper focuses on Kriging in deterministic simulation, the method also applies to other types of metamodels (besides Kriging), and to stochastic simulation. The paper focuses on simulations that require much computer time, so it is important to select a design with a small number of observations. The proposed method is therefore sequential. The novelty of the method is that it accounts for the specific input/output function of the particular simulation model at hand; that is, the method is application-driven or customized. This customization is achieved through cross-validation and jackknifing. The new method is tested through two academic applications, which demonstrate that the method indeed gives better results than either sequential designs based on an approximate Kriging prediction variance formula or designs with prefixed sample sizes.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Kleijnen, J.P.C. & van Beers, W.C.M., 2003. "Application-driven Sequential Designs for Simulation Experiments : Kriging Metamodeling," Other publications TiSEM 1af5ac49-a5f3-4b8a-901c-b, Tilburg University, School of Economics and Management.
  • Handle: RePEc:tiu:tiutis:1af5ac49-a5f3-4b8a-901c-b094a45ea518
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    1. van Beers, W.C.M. & Kleijnen, J.P.C., 2001. "Kriging for Interpolation in Random Simulation," Other publications TiSEM e007110c-e224-484d-92f0-c, Tilburg University, School of Economics and Management.
    2. Kleijnen, Jack P. C. & Sargent, Robert G., 2000. "A methodology for fitting and validating metamodels in simulation," European Journal of Operational Research, Elsevier, vol. 120(1), pages 14-29, January.
    3. Sungmin Park & John W. Fowler & Gerald T. Mackulak & J. Bert Keats & W. Matthew Carlyle, 2002. "D-Optimal Sequential Experiments for Generating a Simulation-Based Cycle Time-Throughput Curve," Operations Research, INFORMS, vol. 50(6), pages 981-990, December.
    4. W C M van Beers & J P C Kleijnen, 2003. "Kriging for interpolation in random simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(3), pages 255-262, March.
    5. B. J. A. Mertens, 2001. "Downdating: Interdisciplinary Research Between Statistics and Computing," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(3), pages 358-366, November.
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