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Stochastic kriging for simulation metamodeling

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>03/2010
<mark>Journal</mark>Operations Research
Issue number2
Number of pages12
Pages (from-to)371-382
Publication StatusPublished
<mark>Original language</mark>English


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.