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

L. Jeff Hong; Zhiyuan Huang; Henry Lam

doi:10.1109/WSC.2016.7822106

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

L. Jeff Hong
, Zhiyuan Huang
, Henry Lam

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

10 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - Winter Simulation Conference
Publisher	IEEE
Pages	389-400
ISBN (Print)	9781509044863
DOIs	https://doi.org/10.1109/WSC.2016.7822106
Publication status	Published - Dec 2016
Event	2016 Winter Simulation Conference, WSC 2016 - Arlington, United States Duration: 11 Dec 2016 → 14 Dec 2016 https://informs-sim.org/wsc16papers/by_area.html http://meetings2.informs.org/wordpress/wintersim2016/

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Name
ISSN (Print)	0891-7736

Conference

Conference	2016 Winter Simulation Conference, WSC 2016
Place	United States
City	Arlington
Period	11/12/16 → 14/12/16
Internet address	https://informs-sim.org/wsc16papers/by_area.html http://meetings2.informs.org/wordpress/wintersim2016/

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AB - 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.

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M3 - RGC 32 - Refereed conference paper (with host publication)

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SP - 389

EP - 400

BT - Proceedings - Winter Simulation Conference

PB - IEEE

T2 - 2016 Winter Simulation Conference, WSC 2016

Y2 - 11 December 2016 through 14 December 2016

ER -

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

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