Combinatorial Optimization Reading Group - Matheus Ota

Title: Data-Driven Chance Constrained Programs over Wasserstein Balls

Abstract: In many real-world applications, precise problem data is not available to the decision maker. One way to handle this uncertainty is by using chance-constraints, where the probability that at least one constraint is violated is bounded above by some parameter. However, such an approach assumes that the decision maker has access to the true probability distribution which governs the data behavior. In order to weaken such an assumption, the literature defined distributionally robust chance-constrained programs (DRCCP). In this model, we have a set of distributions, called the ambiguity set, and the upper bound on the constraint violation probability should hold for all probability distributions in this ambiguity set. One common ambiguity set is based on a Wasserstein ball centered around an empirical distribution. In this talk, I will introduce all the previously mentioned concepts and discuss an approach to model DRCCP's so that they can be solved with standard optimization softwares. Computational experiments show the advantages of using DRCCP's over classical chance-constraints