Stochastic kriging for simulation metamodeling

Bruce E Ankenman*, Barry L Nelson, Jeremy C Staum

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

332 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)371-382
Number of pages12
JournalOperations Research
Volume58
Issue number2
DOIs
StatePublished - Mar 1 2010

Keywords

  • Simulation: design of experiments
  • Statistical analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research

Fingerprint Dive into the research topics of 'Stochastic kriging for simulation metamodeling'. Together they form a unique fingerprint.

Cite this