Approximating data-driven joint chance-constrained programs via uncertainty set construction

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

3 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - Winter Simulation Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages389-400
ISBN (Print)9781509044863
Publication statusPublished - Dec 2016

Publication series

Name
ISSN (Print)0891-7736

Conference

Title2016 Winter Simulation Conference, WSC 2016
PlaceUnited States
CityWashington
Period11 - 14 December 2016

Abstract

We study the use of robust optimization (RO) in approximating joint chance-constrained programs (CCP), in situations where only limited data, or Monte Carlo samples, are available in inferring the underlying probability distributions. We introduce a procedure to construct uncertainty set in the RO problem that translates into provable statistical guarantees for the joint CCP. This procedure relies on learning the high probability region of the data and controlling the region's size via a reformulation as quantile estimation. We show some encouraging numerical results.

Citation Format(s)

Approximating data-driven joint chance-constrained programs via uncertainty set construction. / Hong, L. Jeff; Huang, Zhiyuan; Lam, Henry.

Proceedings - Winter Simulation Conference. Institute of Electrical and Electronics Engineers Inc., 2016. p. 389-400.

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