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Sensitivity analysis of simulation experiments : Regression analysis and statistical design

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  • Kleijnen, J.P.C.

    (Tilburg University, School of Economics and Management)

Abstract

This tutorial gives a survey of strategic issues in the statistical design and analysis of experiments with deterministic and random simulation models. These issues concern validation, what-if analysis, optimization, and so on. The analysis uses regression models and least-squares algorithms. The design uses classical experimental designs such as 2k−p factorials, which are more efficient than one at a time designs are. Moreover, classical designs make it possible to estimate interactions among inputs to the simulation. Simulation models may be optimized through response surface methodology, which combines steepest ascent with regression analysis and experimental design. If there are very many inputs, then special techniques such as group screening and sequential bifurcation are useful. Several applications are discussed.
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Suggested Citation

  • Kleijnen, J.P.C., 1990. "Sensitivity analysis of simulation experiments : Regression analysis and statistical design," Other publications TiSEM 25aee5db-38eb-4e05-b032-2, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:25aee5db-38eb-4e05-b032-2f126e4e4b09
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    References listed on IDEAS

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    1. Kleijnen, J.P.C., 1990. "Statistics and deterministic simulation models : Why not?," Research Memorandum FEW 435, Tilburg University, School of Economics and Management.
    2. Kleijnen, J.P.C., 1988. "Simulation and optimization in production planning : A case study (Version 2)," Research Memorandum FEW 308, Tilburg University, School of Economics and Management.
    3. R. W. Conway, 1963. "Some Tactical Problems in Digital Simulation," Management Science, INFORMS, vol. 10(1), pages 47-61, October.
    4. Kleijnen, Jack P. C. & Standridge, Charles R., 1988. "Experimental design and regression analysis in simulation: An FMS case study," European Journal of Operational Research, Elsevier, vol. 33(3), pages 257-261, February.
    5. Bettonvil, B.W.M. & Kleijnen, J.P.C., 1991. "Identifying the important factors in simulation models with many factors," Research Memorandum FEW 498, Tilburg University, School of Economics and Management.
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    15. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
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