Data-Driven Chance Constrained Programs over Wasserstein Balls

In the era of modern business analytics, data-driven optimization has emerged as a popular modeling paradigm to transform data into decisions. By constructing an ambiguity set potential data-generating distributions and subsequently hedging against all member within this set, effectively combats with which real-life sets are plagued. Chen et al. (2022) study data-driven, chance-constrained programs in decision be feasible high probability under every distribution Wasserstein ball centered at empirical distribution. The authors show that problem admits exact deterministic reformulation mixed-integer conic program demonstrate (in numerical experiments) compares favorably several state-of-the-art schemes.