Robust optimization in simulation

Taguchi and Response Surface Methodology

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

Research output: Contribution to journalArticleScientificpeer-review

248 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)52-59
JournalInternational Journal of Production Economics
Volume125
Issue number1
Publication statusPublished - 2010

Fingerprint

Economics
Robust optimization
Simulation
Response surface methodology
Methodology
Economic order quantity
Inventory model
Order quantity

Cite this

@article{6a101dae10b040af815f0e7c0eceb6be,
title = "Robust optimization in simulation: Taguchi and Response Surface Methodology",
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.",
author = "G. Dellino and Kleijnen, {Jack P.C.} and C. Meloni",
note = "Appeared earlier as CentER DP 2008-69",
year = "2010",
language = "English",
volume = "125",
pages = "52--59",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier Science BV",
number = "1",

}

Robust optimization in simulation : Taguchi and Response Surface Methodology. / Dellino, G.; Kleijnen, Jack P.C.; Meloni, C.

In: International Journal of Production Economics, Vol. 125, No. 1, 2010, p. 52-59.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Robust optimization in simulation

T2 - Taguchi and Response Surface Methodology

AU - Dellino, G.

AU - Kleijnen, Jack P.C.

AU - Meloni, C.

N1 - Appeared earlier as CentER DP 2008-69

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

M3 - Article

VL - 125

SP - 52

EP - 59

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

IS - 1

ER -