A Converging Benders’ Decomposition Algorithm for Two-Stage Mixed-Integer Recourse Models

Novel Optimality Cuts for Two-Stage Stochastic Mixed-Integer Programs The applicability and use of two-stage stochastic mixed-integer programs is well-established, thus calling efficient decomposition algorithms to solve them. Such typically rely on optimality cuts approximate the expected second stage cost function from below. In “A Converging Benders’ Decomposition Algorithm Recourse Models,” van der Laan Romeijnders derive a new family that sufficiently rich identify optimal solution in general. That is, general decision variables are allowed both stages, all data elements be random. Moreover, these require computations decompose by scenario, thus, they can computed efficiently. Van demonstrate potential their approach range problem instances, including DCAP instances SIPLIB.