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 …

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 …

This paper proposes a distributionally robust optimization model for solving unit commitment
(UC) problems considering volatile wind power generation. The uncertainty of wind power is …

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 …

In this paper we survey the primary research, both theoretical and applied, in the area of
robust optimization (RO). Our focus is on the computational attractiveness of RO …

In this paper we focus on a linear optimization problem with uncertainties, having
expectations in the objective and in the set of constraints. We present a modular framework …

The growing uncertainty associated with the increasing penetration of wind and solar power
generation has presented new challenges to the operation of large-scale electric power …

Distributionally robust optimization (DRO) is an emerging and effective method to address
the inexactness of probability distributions of uncertain parameters in decision-making under …

In this paper, we introduce an approach for constructing uncertainty sets for robust
optimization using new deviation measures for random variables termed the forward and …