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Design of Experiments: An Overview

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Author Info
Kleijnen, J.P.C. (Tilburg University, Center for Economic Research)

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Abstract

Design Of Experiments (DOE) is needed for experiments with real-life systems, and with either deterministic or random simulation models. This contribution discusses the different types of DOE for these three domains, but focusses on random simulation. DOE may have two goals: sensitivity analysis including factor screening and optimization. This contribution starts with classic DOE including 2k-p and Central Composite designs. Next, it discusses factor screening through Sequential Bifurcation. Then it discusses Kriging including Latin Hyper cube Sampling and sequential designs. It ends with optimization through Generalized Response Surface Methodology and Kriging combined with Mathematical Programming, including Taguchian robust optimization.

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Publisher Info
Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2008-70.

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Date of creation: 2008
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Handle: RePEc:dgr:kubcen:200870

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Web page: http://center.uvt.nl

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Find related papers by JEL classification:
C0 - Mathematical and Quantitative Methods - - General
C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General
C9 - Mathematical and Quantitative Methods - - Design of Experiments

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  1. Dellino, G. & Kleijnen, J.P.C. & Meloni, C., 2008. "Robust Optimization in Simulation: Taguchi and Response Surface Methodology," Discussion Paper 2008-69, Tilburg University, Center for Economic Research. [Downloadable!]
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This page was last updated on 2009-11-25.

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