Improving prediction from stochastic simulation via model discrepancy learning

Henry Lam, Xinyu Zhang, Matthew Plumlee

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

Abstract

Stochastic simulation is an indispensable tool in operations and management applications. However, simulation models are only approximations to reality, and often bear discrepancies with the generating processes of real output data. We investigate a framework to statistically learn these discrepancies under the presence of data on past implemented system configurations, which allows us to improve prediction using simulation models. We focus on the case of general continuous output data that generalizes previous work. Our approach utilizes (a combination of) regression analysis and optimization formulations constrained on suitable summary statistics. We demonstrate our approach with a numerical example.

Original languageEnglish (US)
Title of host publication2017 Winter Simulation Conference, WSC 2017
EditorsVictor Chan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1808-1819
Number of pages12
ISBN (Electronic)9781538634288
DOIs
StatePublished - Jun 28 2017
Event2017 Winter Simulation Conference, WSC 2017 - Las Vegas, United States
Duration: Dec 3 2017Dec 6 2017

Publication series

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

Other

Other2017 Winter Simulation Conference, WSC 2017
CountryUnited States
CityLas Vegas
Period12/3/1712/6/17

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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  • Cite this

    Lam, H., Zhang, X., & Plumlee, M. (2017). Improving prediction from stochastic simulation via model discrepancy learning. In V. Chan (Ed.), 2017 Winter Simulation Conference, WSC 2017 (pp. 1808-1819). (Proceedings - Winter Simulation Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC.2017.8247918