We study statistical inference and distributionally robust solution methods for stochastic
optimization problems, focusing on confidence intervals for optimal values and solutions that …

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 …

The last decade witnessed an explosion in the availability of data for operations research
applications. Motivated by this growing availability, we propose a novel schema for utilizing …

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

We propose a data-based method to solve a multi-stage stochastic optimal power flow (OPF)
problem based on limited information about forecast error distributions. The framework …