Uncertainty is prevalent in engineering design and data-driven problems and, more broadly,
in decision making. Due to inherent risk-averseness and ambiguity about assumptions, it is …

A key step in solving minimax distributionally robust optimization (DRO) problems is to
reformulate the inner maximization wrt probability measure as a semiinfinite programming …

In this paper, we propose a discretization scheme for the two-stage stochastic linear
complementarity problem (LCP) where the underlying random data are continuously …

This paper studies the risk management of a battery bidding in both day-ahead and intraday
markets arising from the uncertain nature of electricity prices. To this end, a coherent risk …

Uncertainty is prevalent in engineering design, statistical learning, and decision making
broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to …

This article considers a stochastic vehicle routing problem with probability constraints. The
probability that customers are served before their (uncertain) deadlines must be higher than …

It is common to use minimax rules to make planning decisions when there is great
uncertainty about what may happen in the future. Using minimax rules avoids the need to …

Discrete approximation of probability distributions is an important topic in stochastic
programming. In this paper, we extend the research on this topic to distributionally robust …

In this paper, we consider a facility location problem where customer demand constitutes
considerable uncertainty, and where complete information on the distribution of the …

This paper focuses on a class of variational inequalities (VIs), where the map defining the VI
is given by the component-wise conditional value-at-risk (CVaR) of a random function. We …