Input uncertainty and indifference-zone ranking and selection

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - Winter Simulation Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages414-424
Volume2016-February
ISBN (Print)9781467397438
Publication statusPublished - Dec 2015

Publication series

Name
Volume2016-February
ISSN (Print)0891-7736

Conference

TitleWinter Simulation Conference, WSC 2015
PlaceUnited States
CityHuntington Beach
Period6 - 9 December 2015

Abstract

The indifference-zone (IZ) formulation of ranking and selection (R&S) is the foundation of many procedures that have been useful for choosing the best among a finite number of simulated alternatives. Of course, simulation models are imperfect representations of reality, which means that a simulation-based decision, such as choosing the best alternative, is subject to model risk. In this paper we explore the impact of model risk due to input uncertainty on IZ R&S. Input uncertainty is the result of having estimated (fit) the simulation input models to observed real-world data. We find that input uncertainty may force the user to revise, or even abandon, their objectives when employing a R and S procedure, or it may have very little effect on selecting the best system even when the marginal input uncertainty is substantial.

Citation Format(s)

Input uncertainty and indifference-zone ranking and selection. / Song, Eunhye; Nelson, Barry L.; Hong, L. Jeff.

Proceedings - Winter Simulation Conference. Vol. 2016-February Institute of Electrical and Electronics Engineers Inc., 2015. p. 414-424 7408183.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review