Stochastic kriging for simulation metamodeling

Ankenman, Bruce E. and Nelson, Barry L. and Staum, Jeremy (2010) Stochastic kriging for simulation metamodeling. Operations Research, 58 (2). pp. 371-382. ISSN 0030-364X

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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.

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Journal Article
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Operations Research
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14 Jun 2013 13:11
Last Modified:
21 Nov 2022 23:54