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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)


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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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2004-16.

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Date of creation: 2004
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Handle: RePEc:dgr:kubcen:200416

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Keywords: experimental design; simulation; sensitivity analysis; regression analysis; risk analysis; uncertainty;

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  1. Angun, M.E. & Gürkan, G. & Hertog, D. den & Kleijnen, J.P.C., 2002. "Response surface methodology revisited," Open Access publications from Tilburg University urn:nbn:nl:ui:12-91399, Tilburg University.
  2. Kleijnen, J.P.C., 2004. "Design and Analysis of Monte Carlo Experiments," Discussion Paper 2004-17, Tilburg University, Center for Economic Research.
  3. Kleijnen, J.P.C., 1997. "Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models," Discussion Paper 1997-52, Tilburg University, Center for Economic Research.
  4. Kleijnen, Jack P. C. & van Beers, Wim C. M., 2005. "Robustness of Kriging when interpolating in random simulation with heterogeneous variances: Some experiments," European Journal of Operational Research, Elsevier, vol. 165(3), pages 826-834, September.
  5. 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.
  6. Groenendaal, W.J.H. van, 1998. "The Economic Appraisal of Natural Gas Projects," Open Access publications from Tilburg University urn:nbn:nl:ui:12-3804778, Tilburg University.
  7. Kleijnen, J.P.C. & Smits, M.T., 2003. "Performance metrics in supply chain management," Open Access publications from Tilburg University urn:nbn:nl:ui:12-111582, Tilburg University.
  8. Bettonvil, B. & Kleijnen, J.P.C., 1990. "Measurement scales and resolution IV designs," Open Access publications from Tilburg University urn:nbn:nl:ui:12-365617, Tilburg University.
  9. 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.
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Cited by:
  1. Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.
  2. Happe, Kathrin & Kellermann, Konrad, 2007. "DIESE MODELLE SIND ZU KOMPLEX!-ODER DOCH NICHT?: EXPERIMENTELLES DESIGN UND METAMODELLIERUNG ALS MOGLICHER WEG, DAS KOMMUNIKATIONSPROBLEM AGENTENBASIERTER MODELLE IN DER POLITIKANALYSE ZU LOSEN (Germa," 47th Annual Conference, Weihenstephan, Germany, September 26-28, 2007 7613, German Association of Agricultural Economists (GEWISOLA).
  3. Shi, Wen & Liu, Zhixue & Shang, Jennifer & Cui, Yujia, 2013. "Multi-criteria robust design of a JIT-based cross-docking distribution center for an auto parts supply chain," European Journal of Operational Research, Elsevier, vol. 229(3), pages 695-706.
  4. Y. Yang & L. Wang, 2010. "A Review of Modelling Tools for Implementation of the EU Water Framework Directive in Handling Diffuse Water Pollution," Water Resources Management, Springer, vol. 24(9), pages 1819-1843, July.
  5. Collan, Mikael, 2004. "Giga-Investments: Modelling the Valuation of Very Large Industrial Real Investments," MPRA Paper 4328, University Library of Munich, Germany.
  6. Strang, Kenneth David, 2012. "Importance of verifying queue model assumptions before planning with simulation software," European Journal of Operational Research, Elsevier, vol. 218(2), pages 493-504.
  7. Marrel, Amandine & Iooss, Bertrand & Van Dorpe, François & Volkova, Elena, 2008. "An efficient methodology for modeling complex computer codes with Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4731-4744, June.
  8. Hachicha, Wafik & Ammeri, Ahmed & Masmoudi, Faouzi & Chachoub, Habib, 2010. "A comprehensive literature classification of simulation optimisation methods," MPRA Paper 27652, University Library of Munich, Germany.


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