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 …

We provide an exact deterministic reformulation for data-driven, chance-constrained
programs over Wasserstein balls. For individual chance constraints as well as joint chance …

Most of classical stochastic programming assumes that the distribution of uncertain
parameters are known, and this distribution is an input to the model. In many applications …

Adaptive robust optimization problems are usually solved approximately by restricting the
adaptive decisions to simple parametric decision rules. However, the corresponding …

This monograph develops a comprehensive statistical learning framework that is robust to
(distributional) perturbations in the data using Distributionally Robust Optimization (DRO) …

We study stochastic programs where the decision maker cannot observe the distribution of
the exogenous uncertainties but has access to a finite set of independent samples from this …

This paper proposes a data-driven optimization method to solve the integrated energy and
reserve dispatch problem with variable and correlated renewable energy generation. The …

The pre-positioning problem is an important part of emergency supply. Pre-positioning
decisions must be made before disasters occur under high uncertainty and only limited …