We consider a reliable network design problem under uncertain edge failures. Our goal is to select a minimum-cost subset of edges in the network to connect multiple terminals together with high probability. This problem can be seen as a stochastic variant of the Steiner tree problem. We propose two scenario-based Steiner cut formulations, study the strength of the proposed valid inequalities, a...

We introduce a chance constrained optimization model for the fulfillment of guaranteed display Internet advertising campaigns. The proposed formulation for the allocation of display inventory takes into account the uncertainty of the supply of Internet viewers. We discuss and present theoretical and computational features of the model via Monte Carlo sampling and convex approximations. Theoreti...

Introduction: A chance constrained optimization problem involves constraints with stochastic data that are required to be satisfied with a pre-specified probability. When the underlying distribution of the stochastic data is not known precisely, an often used model is to require the chance constraints to hold for all distributions in a given family. Such a problem is known as a distributionally...

Analyzing warehouse performance across different environments is critical to improving overall productivity and reducing costs. Although two-stage DEA estimators have been shown to be statistically consistent, the finite sample bias of DEA in the first stage carries over to the secondstage regression, which causes bias in the estimated coefficients of the contextual variables. The bias is parti...