Integrated chance constraints: Reduced forms and an algorithm

We consider integrated chance constraints (ICCs), which provide quantitative alternatives for traditional chance constraints. We derive explicit polyhedral descriptions for the convex feasible sets induced by ICCs, for the case that the underlying distribution is discrete. Based on these reduced forms, we propose an efficient algorithm for this problem class. The relation to conditional value-at-risk models and (simple) recourse models is discussed, leading to a special purpose algorithm for simple recourse models with discretely distributed technology matrix. For both algorithms, numerical results are presented.