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

This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …

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

This paper studies a distributionally robust chance constrained program (DRCCP) with
Wasserstein ambiguity set, where the uncertain constraints should be satisfied with a …

We provide an exact deterministic reformulation for data-driven, chance-constrained
programs over Wasserstein balls. For individual chance constraints as well as joint chance …

We propose piecewise linear kernel-based support vector clustering (SVC) as a new
approach tailored to data-driven robust optimization. By solving a quadratic program, the …

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

The uncertainty associated with renewable energy sources introduces significant challenges
in optimal power flow (OPF) analysis. A variety of new approaches have been proposed that …

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

Abstract An effective Emergency Medical Service (EMS) system can provide medical relief
supplies for common emergencies (fire, accident, etc.) or large-scale disasters (earthquake …