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IIE Transactions Volume 38, 2006 - Issue 4
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ORIGINAL ARTICLES

A review on design, modeling and applications of computer experiments

Victoria C.P. Chen Department of Industrial and Manufacturing Systems Engineering, The University of Texas at Arlington, Campus Box 19017, Arlington, TX, 76019-0017, USA E-mail: [email protected]
,
Kwok-Leung Tsui School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0205, USA E-mail: [email protected]
,
Russell R. Barton The Mary Jean and Frank P. Smeal College of Business Administration, The Pennsylvania State University, 801 Business Administration Building, University Park, PA, 16802, USA E-mail: [email protected]
&
Martin Meckesheimer The Boeing Company, P.O. Box 3707, MC 71-21, Seattle, WA, 98124-2207, USA E-mail: [email protected]
Pages 273-291 | Received 01 May 2004, Accepted 01 Jan 2005, Published online: 23 Feb 2007
 
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In this paper, we provide a review of statistical methods that are useful in conducting computer experiments. Our focus is on the task of metamodeling, which is driven by the goal of optimizing a complex system via a deterministic simulation model. However, we also mention the case of a stochastic simulation, and examples of both cases are discussed. The organization of our review first presents several engineering applications, it then describes approaches for the two primary tasks of metamodeling: (i) selecting an experimental design; and (ii) fitting a statistical model. Seven statistical modeling methods are included. Both classical and newer experimental designs are discussed. Finally, our own computational study tests the various metamodeling options on two two-dimensional response surfaces and one ten-dimensional surface.

Acknowledgements

VCPC was partially supported by NSF grant DMI 0100123 and a Technology for Sustainable Environment grant under the US EPA's Science to Achieve Results Program (contract R-82820701-0). KLT was partially supported by NSF grants DMI 9908013 and DMI 0100123 and The Logistics Institute–Asia Pacific in Singapore. RRB was partially supported by NSF grants DMI 970040 and DMI 0084918.

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