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 …

Facility location, supply inventory and distribution, and evacuation planning are key
operational functions in a humanitarian relief network, it is critical to integrate these three …

We propose a novel, optimization-based method that takes into account the objective and
problem structure for reducing the number of scenarios, m, needed for solving two-stage …

This paper investigates how the choice of stochastic approaches and distribution
assumptions impacts strategic investment decisions in energy planning problems. We …

We study policy optimization for the feature-based newsvendor, which seeks an end-to-end
policy that renders an explicit map** from features to ordering decisions. Most existing …

In recent years, robust Markov decision processes (MDPs) have emerged as a prominent
modeling framework for dynamic decision problems affected by uncertainty. In contrast to …

The design of data-driven formulations for machine learning and decision-making with good
out-of-sample performance is a key challenge. The observation that good in-sample …

We introduce a new class of distributionally robust optimization problems under decision-
dependent ambiguity sets. In particular, as our ambiguity sets, we consider balls centered on …

Recent research on distributionally robust (DR) machine scheduling has used a variety of
approaches to describe the region of ambiguity of uncertain processing times by imposing …