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

Distributionally robust stochastic optimization (DRSO) is an approach to optimization under
uncertainty in which, instead of assuming that there is a known true underlying probability …

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 study how delivery data can be applied to improve the on-time performance of last-mile
delivery services. Motivated by the delivery operations and data of a food delivery service …

Wasserstein distributionally robust optimization (DRO) aims to find robust and generalizable
solutions by hedging against data perturbations in Wasserstein distance. Despite its recent …

We study a distributionally robust version of the classical capacitated facility location
problem with a distributional ambiguity set defined as a Wasserstein ball around an …

New technologies and innovative business models are leading to connected, shared,
autonomous, and electric solutions for the tomorrow of urban transport and logistics (UTL) …

We study a variant of the capacitated vehicle routing problem (CVRP), which asks for the
cost-optimal delivery of a single product to geographically dispersed customers through a …

Teleconsultation service plays an important role in the Chinese healthcare service provision
especially during the period of the COVID-19 pandemic. We propose a two-stage …

Estimators based on Wasserstein distributionally robust optimization are obtained as
solutions of min-max problems in which the statistician selects a parameter minimizing the …