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

Static robust optimization (RO) is a methodology to solve mathematical optimization
problems with uncertain data. The objective of static RO is to find solutions that are immune …

Many decision problems in science, engineering, and economics are affected by uncertain
parameters whose distribution is only indirectly observable through samples. The goal of …

We consider stochastic programs where the distribution of the uncertain parameters is only
observable through a finite training dataset. Using the Wasserstein metric, we construct a …

Robust optimization is a young and active research field that has been mainly developed in
the last 15 years. Robust optimization is very useful for practice, since it is tailored to the …

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 …

This paper provides an overview of developments in robust optimization since 2007. It seeks
to give a representative picture of the research topics most explored in recent years …

We develop a modular and tractable framework for solving an adaptive distributionally
robust linear optimization problem, where we minimize the worst-case expected cost over an …

This paper proposes a novel data-driven robust optimization framework that leverages the
power of machine learning and big data analytics for decision-making under uncertainty. By …

We present a new distributionally robust optimization model called robust stochastic
optimization (RSO), which unifies both scenario-tree-based stochastic linear optimization …