TY - JOUR

T1 - Stochastic kriging for simulation metamodeling

AU - Ankenman, Bruce E.

AU - Nelson, Barry L.

AU - Staum, Jeremy

PY - 2010/3

Y1 - 2010/3

N2 - 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.

AB - 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.

KW - simulation

KW - design of experiments

KW - statistical analysis

U2 - 10.1287/opre.1090.0754

DO - 10.1287/opre.1090.0754

M3 - Journal article

VL - 58

SP - 371

EP - 382

JO - Operations Research

JF - Operations Research

SN - 0030-364X

IS - 2

ER -