Input uncertainty and indifference-zone ranking and selection

Eunhye Song, Barry L. Nelson, L. Jeff Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations


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.

Original languageEnglish (US)
Title of host publication2015 Winter Simulation Conference, WSC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages11
ISBN (Electronic)9781467397438
StatePublished - Feb 16 2016
EventWinter Simulation Conference, WSC 2015 - Huntington Beach, United States
Duration: Dec 6 2015Dec 9 2015

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


OtherWinter Simulation Conference, WSC 2015
Country/TerritoryUnited States
CityHuntington Beach

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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