Robust simulation of stochastic systems with input uncertainties modeled by statistical divergences

Zhaolin Hu, L. Jeff Hong

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

5 Scopus citations

Abstract

Simulation is often used to study stochastic systems. A key step of this approach is to specify a distribution for the random input. This is called input modeling, which is important and even critical for simulation study. However, specifying a distribution precisely is usually difficult and even impossible in practice. This issue is called input uncertainty in simulation study. In this paper we study input uncertainty when using simulation to estimate important performance measures: expectation, probability, and value-at-risk. We propose a robust simulation (RS) approach, which assumes the real distribution is contained in a certain ambiguity set constructed using statistical divergences, and simulates the maximum and the minimum of the performance measures when the distribution varies in the ambiguity set. We show that the RS approach is computationally tractable and the corresponding results can disclose important information about the systems, which may help decision makers better understand the systems.

Original languageEnglish (US)
Title of host publication2015 Winter Simulation Conference, WSC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages643-654
Number of pages12
ISBN (Electronic)9781467397438
DOIs
StatePublished - Feb 16 2016
Externally publishedYes
EventWinter Simulation Conference, WSC 2015 - Huntington Beach, United States
Duration: Dec 6 2015Dec 9 2015

Publication series

NameProceedings - Winter Simulation Conference
Volume2016-February
ISSN (Print)0891-7736

Conference

ConferenceWinter Simulation Conference, WSC 2015
Country/TerritoryUnited States
CityHuntington Beach
Period12/6/1512/9/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

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