Stochastic Kriging for Simulation Metamodeling

Bruce Ankenman (), Barry L. Nelson () and Jeremy Staum ()
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Bruce Ankenman: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208
Barry L. Nelson: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208
Jeremy Staum: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208

Operations Research, 2010, vol. 58, issue 2, 371-382

Abstract: We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Our goal is to provide flexible, interpolation-based metamodels of simulation output performance measures as functions of the controllable design or decision variables, or uncontrollable environmental variables. To accomplish this, we characterize both the intrinsic uncertainty inherent in a stochastic simulation and the extrinsic uncertainty about the unknown response surface. We use tractable examples to demonstrate why it is critical to characterize both types of uncertainty, derive general results for experiment design and analysis, and present a numerical example that illustrates the stochastic kriging method.

Keywords: simulation; design of experiments; statistical analysis (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (81)

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