Metamodel-based robust simulation-optimization: An overview

G. Dellino, C. Meloni, J.P.C. Kleijnen

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

15 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationUncertainty Management in Simulation-Optimization of Complex Systems
Subtitle of host publicationAlgorithms and Applications
EditorsGabriella Dellino, Carlo Meloni
PublisherSpringer VS
ISBN (Electronic)1387666X
ISBN (Print)9781489975461
Publication statusPublished - 2015

Publication series

NameOperations Research/Computer Science Interfaces Series
PublisherSpringer US


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