Data-driven distributionally robust chance-constrained optimization with Wasserstein metric

• Published in: Journal of global optimization Vol. 79; no. 4; pp. 779 - 811
• Main Authors: Ji, Ran, Lejeune, Miguel A.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2021; Springer; Springer Nature B.V
• Subjects: Computer Science; Constraints; Integer programming; Investment analysis; Knapsack problem; Linear programming; Mathematics; Mathematics and Statistics; Mixed integer; Operations Research/Decision Theory; Optimization; Real Functions; Robustness; Series (mathematics); Tightness; Chance-constrained programming; Mixed-integer programming; Distributionally robust optimization; Wasserstein metric

We study distributionally robust chance-constrained programming ( DRCCP ) optimization problems with data-driven Wasserstein ambiguity sets. The proposed algorithmic and reformulation framework applies to all types of distributionally robust chance-constrained optimization problems subjected to indi...