eprintid: 750
rev_number: 11
eprint_status: archive
userid: 17
dir: disk0/00/00/07/50
datestamp: 2011-08-01 11:26:11
lastmod: 2011-08-04 07:30:21
status_changed: 2011-08-01 11:26:11
type: article
metadata_visibility: no_search
item_issues_count: 0
creators_name: Dellino, Gabriella
creators_name: Kleijnen, Jack P.C.
creators_name: Meloni, Carlo
creators_id: gabriella.dellino@imtlucca.it
creators_id: 
creators_id: 
title: Robust optimization in simulation: Taguchi and Krige combined
ispublished: pub
subjects: HA
subjects: HB
subjects: QA
divisions: CSA
full_text_status: none
keywords: statistics; design of experiments; inventory production; simulation; decision analysis; risk
note: Published online before print
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. 
date: 2011-07
publication: Informs Journal on Computing
publisher: Informs
id_number: 10.1287/ijoc.1110.0465
refereed: TRUE
issn: 1091-9856
official_url: http://joc.journal.informs.org/cgi/content/abstract/ijoc.1110.0465v1
citation:   Dellino, Gabriella and Kleijnen, Jack P.C. and Meloni, Carlo  Robust optimization in simulation: Taguchi and Krige combined.  Informs Journal on Computing.   ISSN 1091-9856      (2011)