A statistical perspective on linear programs with uncertain parameters

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

View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - Winter Simulation Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3690-3701
Volume2016-February
ISBN (Print)9781467397438
StatePublished - 16 Feb 2016

Publication series

Name
Volume2016-February
ISSN (Print)0891-7736

Conference

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

Abstract

We consider linear programs where some parameters in the objective functions are unknown but data are available. For a risk-averse modeler, the solutions of these linear programs should be picked in a way that can perform well for a range of likely scenarios inferred from the data. The conventional approach uses robust optimization. Taking the optimality gap as our loss criterion, we argue that this approach can be high-risk, in the sense that the optimality gap can be large with significant probability. We then propose two computationally tractable alternatives: The first uses bootstrap aggregation, or so-called bagging in the statistical learning literature, while the second uses Bayes estimator in the decision-theoretic framework. Both are simulation-based schemes that aim to improve the distributional behavior of the optimality gap by reducing its frequency of hitting large values.

Citation Format(s)

A statistical perspective on linear programs with uncertain parameters. / Hong, L. Jeff; Lam, Henry.

Proceedings - Winter Simulation Conference. Vol. 2016-February Institute of Electrical and Electronics Engineers Inc., 2016. p. 3690-3701 7408527.

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