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Sensitivity analysis of simulation experiments : Regression analysis and statistical design

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  • Kleijnen, J.P.C.

    (Tilburg University, Faculty of Economics)

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.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Kleijnen, J.P.C., 1990. "Sensitivity analysis of simulation experiments : Regression analysis and statistical design," Research Memorandum FEW 440, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiurem:25aee5db-38eb-4e05-b032-2f126e4e4b09
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Kleijnen, J.P.C., 1990. "Statistics and deterministic simulation models : Why not?," Research Memorandum FEW 435, Tilburg University, School of Economics and Management.
    4. Kleijnen, J.P.C., 1988. "Simulation and optimization in production planning : A case study (Version 2)," Other publications TiSEM 97a8a024-2229-4f33-8edb-1, Tilburg University, School of Economics and Management.
    5. R. W. Conway, 1963. "Some Tactical Problems in Digital Simulation," Management Science, INFORMS, vol. 10(1), pages 47-61, October.
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    3. Reis dos Santos, M. Isabel & Reis dos Santos, Pedro M., 2016. "Switching regression metamodels in stochastic simulation," European Journal of Operational Research, Elsevier, vol. 251(1), pages 142-147.
    4. Backus, G. B. C. & Timmer, G. Th. & Dijkhuizen, A. A. & Eidman, V. R. & Vos, F., 1995. "A decision support system for strategic planning on pig farms," Agricultural Economics, Blackwell, vol. 13(2), pages 101-108, November.
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    6. Storlie, Curtis B. & Helton, Jon C., 2008. "Multiple predictor smoothing methods for sensitivity analysis: Description of techniques," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 28-54.
    7. Sallaberry, C.J. & Helton, J.C. & Hora, S.C., 2008. "Extension of Latin hypercube samples with correlated variables," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 1047-1059.
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    9. Salameh, F. & Picot, A. & Chabert, M. & Maussion, P., 2017. "Regression methods for improved lifespan modeling of low voltage machine insulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 200-216.
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    11. Helton, Jon C. & Hansen, Clifford W. & Sallaberry, Cédric J., 2012. "Uncertainty and sensitivity analysis in performance assessment for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 44-63.
    12. Helton, Jon C., 2011. "Quantification of margins and uncertainties: Conceptual and computational basis," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 976-1013.
    13. 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.
    14. Huyet, A.L., 2006. "Optimization and analysis aid via data-mining for simulated production systems," European Journal of Operational Research, Elsevier, vol. 173(3), pages 827-838, September.
    15. Tilottama Chakraborty & Mrinmoy Majumder, 2019. "Application of statistical charts, multi-criteria decision making and polynomial neural networks in monitoring energy utilization of wave energy converters," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(1), pages 199-219, February.
    16. Graham, Tennille & White, Benedict & Pannell, David J., 2003. "Efficiency Policies for Salinity Management: Preliminary Research from a Spatial and Dynamic Metamodel," 2003 Conference (47th), February 12-14, 2003, Fremantle, Australia 57879, Australian Agricultural and Resource Economics Society.
    17. Soumya Ghosh & Mrinmoy Majumder & Manish Pal, 2018. "Application of metaheuristic algorithm to identify priority parameters for the selection of feasible location having optimum wave energy potential," Energy & Environment, , vol. 29(1), pages 3-28, February.

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    Keywords

    Simulation; mathematische statistiek;

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