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An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis

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  • Kleijnen, J.P.C.

    (Tilburg University, Center For Economic Research)

Abstract

Sensitivity analysis may serve validation, optimization, and risk analysis of simulation models.This review surveys classic and modern designs for experiments with simulation models.Classic designs were developed for real, non-simulated systems in agriculture, engineering, etc.These designs assume a few factors (no more than ten factors) with only a few values per factor (no more than five values).These designs are mostly incomplete factorials (e.g., fractionals).The resulting input/output (I/O) data are analyzed through polynomial metamodels, which are a type of linear regression models.Modern designs were developed for simulated systems in engineering, management science, etc.These designs allow many factors (more than 100), each with either a few or many (more than 100) values.These designs include group screening, Latin Hypercube Sampling (LHS), and other space filling designs.Their I/O data are analyzed through second-order polynomials for group screening, and through Kriging models for LHS.
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Suggested Citation

  • Kleijnen, J.P.C., 2004. "An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis," Discussion Paper 2004-16, Tilburg University, Center for Economic Research.
    • Handle: RePEc:tiu:tiucen:8151e500-26af-4fb3-87cc-9800653de852
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    References listed on IDEAS

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    1. Shu Yamada & Dennis Lin, 2002. "Construction of mixed-level supersaturated design," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 56(3), pages 205-214, December.
    2. Joan M. Donohue & Ernest C. Houck & Raymond H. Myers, 1993. "Simulation Designs and Correlation Induction for Reducing Second-Order Bias in First-Order Response Surfaces," Operations Research, INFORMS, vol. 41(5), pages 880-902, October.
    3. Jack P. C. Kleijnen, 1992. "Regression Metamodels for Simulation with Common Random Numbers: Comparison of Validation Tests and Confidence Intervals," Management Science, INFORMS, vol. 38(8), pages 1164-1185, August.
    4. Kleijnen, J.P.C., 2004. "Design and Analysis of Monte Carlo Experiments," Other publications TiSEM 7a12daae-c849-427e-8269-7, Tilburg University, School of Economics and Management.
    5. van Groenendaal, W.J.H., 1998. "The Economic Appraisal of Natural Gas Projects," Other publications TiSEM a0ff517c-2041-4457-adac-7, Tilburg University, School of Economics and Management.
    6. 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.
    7. Russell C. H. Cheng & Jack P. C. Kleijnen, 1999. "Improved Design of Queueing Simulation Experiments with Highly Heteroscedastic Responses," Operations Research, INFORMS, vol. 47(5), pages 762-777, October.
    8. Antoniadis, Anestis & Dinh Tuan Pham, 1998. "Wavelet regression for random or irregular design," Computational Statistics & Data Analysis, Elsevier, vol. 28(4), pages 353-369, October.
    9. J P C Kleijnen & M T Smits, 2003. "Performance metrics in supply chain management," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(5), pages 507-514, May.
    10. Kleijnen, J.P.C. & Smits, M.T., 2003. "Performance metrics in supply chain management," Other publications TiSEM 80777aed-0c9f-4ded-b0bb-f, Tilburg University, School of Economics and Management.
    11. Angun, M.E. & Gürkan, G. & den Hertog, D. & Kleijnen, J.P.C., 2002. "Response surface methodology revisited," Other publications TiSEM 32c35a04-3de9-4dee-a242-6, Tilburg University, School of Economics and Management.
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