Distributionally risk‐receptive and risk‐averse network interdiction problems with general ambiguity set

We introduce generalizations of stochastic network interdiction problem with distributional ambiguity. Specifically, we consider a distributionally risk-averse (or robust) (DRA-NIP) and risk-receptive (DRR-NIP) where leader maximizes follower's minimal expected objective value for either the worst-case or best-case, respectively, probability distribution belonging to ambiguity set (a distributions). The DRA-NIP arises in applications interdicts follower cause delays their supply convoy. In contrast, DRR-NIP provides vulnerability analysis network-user seeks identify vulnerabilities against potential disruptions by an adversary leader) who is receptive risk improving values. present finitely convergent algorithms solving general set. To evaluate performance, provide results our extensive computational experiments performed on instances known (risk-neutral) NIP.