The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …

The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

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 …

Wasserstein distributionally robust optimization (DRO) aims to find robust and generalizable
solutions by hedging against data perturbations in Wasserstein distance. Despite its recent …

Natural disasters are highly unpredictable, with varying degrees of magnitude, and thus
require a reliable and robust humanitarian relief network. Faced with the adverse effects of …

We study a distributionally robust version of the classical capacitated facility location
problem with a distributional ambiguity set defined as a Wasserstein ball around an …

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

Wasserstein distributionally robust optimization (DRO) is an approach to optimization under
uncertainty in which the decision maker hedges against a set of probability distributions …

We consider optimal transport-based distributionally robust optimization (DRO) problems
with locally strongly convex transport cost functions and affine decision rules. Under …

Distributionally robust optimization is a popular modeling paradigm in which the underlying
distribution of the random parameters in a stochastic optimization model is unknown …