Input–Output Uncertainty Comparisons for Discrete Optimization via Simulation

Eunhye Song () and Barry L. Nelson ()
Additional contact information
Eunhye Song: Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802;
Barry L. Nelson: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208

Operations Research, 2019, vol. 67, issue 2, 562-576

Abstract: When input distributions to a simulation model are estimated from real-world data, they naturally have estimation error causing input uncertainty in the simulation output. If an optimization via simulation (OvS) method is applied that treats the input distributions as “correct,” then there is a risk of making a suboptimal decision for the real world, which we call input model risk . This paper addresses a discrete OvS (DOvS) problem of selecting the real-world optimal from among a finite number of systems when all of them share the same input distributions estimated from common input data. Because input uncertainty cannot be reduced without collecting additional real-world data—which may be expensive or impossible—a DOvS procedure should reflect the limited resolution provided by the simulation model in distinguishing the real-world optimal solution from the others. In light of this, our input–output uncertainty comparisons (IOU-C) procedure focuses on comparisons rather than selection : it provides simultaneous confidence intervals for the difference between each system’s real-world mean and the best mean of the rest with any desired probability, while accounting for both stochastic and input uncertainty. To make the resolution as high as possible (intervals as short as possible) we exploit the common input data effect to reduce uncertainty in the estimated differences. Under mild conditions we prove that the IOU-C procedure provides the desired statistical guarantee asymptotically as the real-world sample size and simulation effort increase, but it is designed to be effective in finite samples.

Keywords: optimization via simulation under input uncertainty; common-input-data effect; multiple comparisons with the best (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1287/opre.2018.1796 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:67:y:2019:i:2:p:562-576

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().