In this paper, we survey the primary research on the theory and applications of
distributionally robust optimization (DRO). We start with reviewing the modeling power and …

Abstract Throughout its history, Operational Research has evolved to include methods,
models and algorithms that have been applied to a wide range of contexts. This …

We study distributionally robust optimization (DRO) with Sinkhorn distance--a variant of
Wasserstein distance based on entropic regularization. We derive convex programming …

We study distributionally robust chance-constrained programming (DRCCP) optimization
problems with data-driven Wasserstein ambiguity sets. The proposed algorithmic and …

The robustness of risk measures to changes in underlying loss distributions (distributional
uncertainty) is of crucial importance in making well‐informed decisions. In this paper, we …