eprintid: 746
rev_number: 13
eprint_status: archive
userid: 17
dir: disk0/00/00/07/46
datestamp: 2011-08-01 10:23:29
lastmod: 2011-08-04 07:30:21
status_changed: 2011-08-01 10:23:29
type: article
metadata_visibility: show
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 response surface methodology
ispublished: pub
subjects: HB
subjects: QA
divisions: EIC
full_text_status: public
keywords: Pareto frontier; Bootstrap; Latin hypercube sampling
abstract: Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a `robust' methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by Response Surface Methodology (RSM). George Box originated RSM, and Douglas Montgomery recently extended RSM to robust optimization of real (non-simulated) systems. We combine Taguchi's view with RSM for simulated systems. We illustrate the resulting methodology through classic Economic Order Quantity (EOQ) inventory models, which demonstrate that robust optimization may require order quantities that differ from the classic EOQ.
date: 2010-05
publication: International journal of production economics
volume: 125
number: 1
publisher: Elsevier
pagerange: 52 - 59
id_number: 10.1016/j.ijpe.2009.12.003
refereed: TRUE
issn: 0925-5273
official_url: http://www.sciencedirect.com/science/article/pii/S0925527309004502
citation:   Dellino, Gabriella and Kleijnen, Jack P.C. and Meloni, Carlo  Robust optimization in simulation: Taguchi and response surface methodology.  International journal of production economics, 125 (1).  52 - 59.  ISSN 0925-5273      (2010)  
document_url: http://eprints.imtlucca.it/746/1/Dellino_2010.pdf