Data-Driven Chance Constrained Programs over Wasserstein Balls

被引：60

作者：

Chen, Zhi ^{[1
]}

Kuhn, Daniel ^{[2
]}

Wiesemann, Wolfram ^{[3
]}

机构：

[1] City Univ Hong Kong, Coll Business, Dept Management Sci, Kowloon Tong, Hong Kong, Peoples R China

[2] Ecole Polytech Fed Lausann, Risk Analyt & Optimizat Chair, CH-1015 Lausanne, Switzerland

[3] Imperial Coll London, Imperial Coll Business Sch, South Kensington Campus, London SW7 2AZ, England

来源：

OPERATIONS RESEARCH | 2024年 / 72卷 / 01期

基金：

瑞士国家科学基金会; 英国工程与自然科学研究理事会;

关键词：

distributionally robust optimization; ambiguous chance constraints; Wasserstein distance; DISTRIBUTIONALLY ROBUST OPTIMIZATION;

D O I：

10.1287/opre.2022.2330

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

摘要：

We provide an exact deterministic reformulation for data-driven, chanceconstrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constraints with right-hand-side uncertainty, our reformulation amounts to a mixed-integer conic program. In the special case of a Wasserstein ball with the 1-norm or the ???-norm, the cone is the nonnegative orthant, and the chance-constrained program can be reformulated as a mixed-integer linear program. Our reformulation compares favorably to several state-of-the-art data-driven optimization schemes in our numerical experiments.

引用

页码：410 / 424

页数：16

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