Data-driven distributionally robust optimization: Intersecting ambiguity sets, performance analysis and tractability

We consider stochastic programs in which the probability distribution of uncertain parameters¬†is unknown and partial information about it can only be captured from limited data. We use distributionally robust optimization (DRO) to model such problems. As opposed to the commonly used approach for DRO problems that suggests creating an ambiguity set by following a specific … Read more

Robust Critical Node Selection by Benders Decomposition

The critical node selection problem (CNP) has important applications in telecommunication, supply chain design, and disease propagation prevention. In practice, the weights on the connections are either uncertain or hard to estimate so recently robust optimization approaches have been considered for CNP. In this article, we address very general uncertainty sets, only requiring a linear … Read more