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

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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.
Original languageEnglish
Place of PublicationTilburg
PublisherOperations research
Number of pages34
Volume2004-16
Publication statusPublished - 2004

Publication series

NameCentER Discussion Paper
Volume2004-16

Keywords

  • experimental design
  • simulation
  • sensitivity analysis
  • regression analysis
  • risk analysis
  • uncertainty

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    Kleijnen, J. P. C. (2004). An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis. (CentER Discussion Paper; Vol. 2004-16). Operations research.