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 …

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 …

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

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 …

Motivated by a leading taxi operator in Singapore, we consider the idle vehicle pre‐
allocation problem with uncertain demands and other uncertain covariate information such …

We study multistage distributionally robust mixed-integer programs under endogenous
uncertainty, where the probability distribution of stage-wise uncertainty depends on the …

The ever-increasing penetration of renewable energy sources (RESs) has brought
tremendous challenges to power system energy management problems. In this paper, we …

Elective surgery patients have surgery in the operating room (OR), and then recover in one
or more downstream recovery units for several consecutive hours or days after surgery …