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 …

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

Distributionally robust optimization is a paradigm for decision making under uncertainty
where the uncertain problem data are governed by a probability distribution that is itself …

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 aim of this work is not only to give an up-to-date review of perishable inventory models,
but also of the joint key topics of publications from January 2012 until December 2015 in the …

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

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

Energy hub system (EHS) incorporating multiple energy carriers, storage, and renewables
can efficiently coordinate various energy resources to optimally satisfy energy demand …

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 …