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

The robustness of risk measures to changes in underlying loss distributions (distributional
uncertainty) is of crucial importance in making well‐informed decisions. In this paper, we …

This paper studies reinsurance contracting and competition in a continuous-time model with
ambiguity. The market consists of one insurer and two reinsurers, who apply a generalized …

This paper studies a two-stage distributionally robust stochastic linear program under the
type-∞ Wasserstein ball by providing sufficient conditions under which the program can be …

The notion of a Bowley optimum has gained recent popularity as an equilibrium concept in
problems of risk sharing and optimal reinsurance. In this paper, we examine the relationship …

In a chance constrained program (CCP), decision makers seek the best decision whose
probability of violating the uncertainty constraints is within the prespecified risk level. As a …

This paper studies distributionally robust chance constrained programs (DRCCPs), where
the uncertain constraints must be satisfied with at least a probability of a prespecified …

Distributionally robust chance-constrained programs (DR-CCPs) over Wasserstein
ambiguity sets exhibit attractive out-of-sample performance and admit big-M–based mixed …

In the optimization under uncertainty, decision-makers first select a wait-and-see policy
before any realization of uncertainty and then place a here-and-now decision after the …

Lane reservation optimization is important in intelligent transportation systems. Most existing
studies are carried out under deterministic road conditions by assuming constant road travel …