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Robust optimization in simulation: Taguchi and Krige combined

Dellino, Gabriella and Kleijnen, Jack P.C. and Meloni, Carlo Robust optimization in simulation: Taguchi and Krige combined. Informs Journal on Computing, 24 (3). pp. 471-484. ISSN 1091-9856 (2012)

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Abstract

Optimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a "robust" methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world but replaces his statistical techniques by design and analysis of simulation experiments based on Kriging (Gaussian process model); moreover, we use bootstrapping to quantify the variability in the estimated Kriging metamodels. In addition, we combine Kriging with nonlinear programming, and we estimate the Pareto frontier. We illustrate the resulting methodology through economic order quantity (EOQ) inventory models. Our results suggest that robust optimization requires order quantities that differ from the classic EOQ. We also compare our results with results we previously obtained using response surface methodology instead of Kriging.

Item Type: Article
Identification Number: https://doi.org/10.1287/ijoc.1110.0465
Uncontrolled Keywords: statistics; design of experiments; inventory production; simulation; decision analysis; risk
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
Q Science > QA Mathematics
Research Area: Economics and Institutional Change
Depositing User: Users 17 not found.
Date Deposited: 29 Nov 2012 16:39
Last Modified: 29 Nov 2012 16:39
URI: http://eprints.imtlucca.it/id/eprint/1436

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