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Stochastic Kriging for Simulation Metamodeling

Authors:

Bruce Ankenman,

Barry L. Nelson,

Jeremy StaumAuthors Info & Claims

Operations Research, Volume 58, Issue 2

Pages 371 - 382

https://doi.org/10.1287/opre.1090.0754

Published: 01 March 2010 Publication History

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.

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Cited By

Ding LZhang X(2024)Sample and Computationally Efficient Stochastic Kriging in High DimensionsOperations Research10.1287/opre.2022.236772:2(660-683)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1287/opre.2022.2367
Wang WWang YZhang X(2024)Smooth Nested SimulationManagement Science10.1287/mnsc.2022.0020470:12(9031-9057)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1287/mnsc.2022.00204
Jiang GHong LShen H(2024)Real-Time Derivative Pricing and Hedging with Consistent MetamodelsINFORMS Journal on Computing10.1287/ijoc.2023.029236:5(1168-1189)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1287/ijoc.2023.0292
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Stochastic Kriging for Simulation Metamodeling
1. Computing methodologies
  1. Modeling and simulation
2. Mathematics of computing

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Published In

cover image Operations Research

Operations Research Volume 58, Issue 2

March 2010

263 pages

ISSN:0030-364X

Issue’s Table of Contents

Publisher

INFORMS

Linthicum, MD, United States

Publication History

Published: 01 March 2010

Accepted: 01 November 2008

Received: 01 May 2008

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Cited By

Ding LZhang X(2024)Sample and Computationally Efficient Stochastic Kriging in High DimensionsOperations Research10.1287/opre.2022.236772:2(660-683)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1287/opre.2022.2367
Wang WWang YZhang X(2024)Smooth Nested SimulationManagement Science10.1287/mnsc.2022.0020470:12(9031-9057)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1287/mnsc.2022.00204
Jiang GHong LShen H(2024)Real-Time Derivative Pricing and Hedging with Consistent MetamodelsINFORMS Journal on Computing10.1287/ijoc.2023.029236:5(1168-1189)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1287/ijoc.2023.0292
Chen XZhang YXie GZhang J(2024)A Uniform Error Bound for Stochastic Kriging: Properties and Implications on Simulation Experimental DesignACM Transactions on Modeling and Computer Simulation10.1145/368205935:1(1-43)Online publication date: 25-Nov-2024
https://dl.acm.org/doi/10.1145/3682059
Zhang YChen X(2024)Sequential metamodel‐based approaches to level‐set estimation under heteroscedasticityStatistical Analysis and Data Mining10.1002/sam.1169717:3Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1002/sam.11697
Sertkaya ERyzhov I(2023)Approximate Gaussian Process Regression with Pairwise Comparison DataProceedings of the Winter Simulation Conference10.5555/3643142.3643434(3496-3507)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643434
Men JLiu ZWang SWang H(2023)Simulation Optimization with Multiple AttemptsProceedings of the Winter Simulation Conference10.5555/3643142.3643432(3472-3483)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643432
Barton R(2023)Tutorial: Basics of MetamodelingProceedings of the Winter Simulation Conference10.5555/3643142.3643267(1516-1530)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643267
Fadikar ACollier NStevens AOzik JBinois MToh K(2023)Trajectory-Oriented Optimization of Stochastic Epidemiological ModelsProceedings of the Winter Simulation Conference10.5555/3643142.3643245(1244-1255)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643245
Li CGao SDu J(2023)Convergence Analysis of Stochastic Kriging-Assisted Simulation with Random CovariatesINFORMS Journal on Computing10.1287/ijoc.2022.126335:2(386-402)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1287/ijoc.2022.1263
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