Technical Note—Stochastic Optimization with Decisions Truncated by Positively Dependent Random Variables

We study stochastic optimization problems with decisions truncated by random variables. This paper extends existing results in the literature by allowing positively dependent random variables and a two-part fee structure. We develop a transformation technique to convert the original nonconvex problems to equivalent convex ones. We apply our transformation technique to an inventory substitution model with random supply capacities and a two-part fee cost structure. In addition, we extend our results to incorporate the decision maker’s risk attitude.