The Sample Average Approximation Method for Stochastic Programs with Integer Recourse

Abstract

This paper introduces a new cutting plane method for two-stage stochastic mixed-integer programming (SMIP) called Fenchel decomposition (FD). FD uses a class of valid inequalities termed, FD cuts, which are derived based on Fenchel cutting planes from integer programming. First, we derive FD cuts based on both the rst and second-stage variables, and devise an FD algorithm for SMIP and establish nite convergence for binary rst-stage. Second, we derive FD cuts based on the second-stage variables only and use an idea from disjunctive programming to lift the cuts to the higher dimension space including the rst-stage variables. We then devise an alternative algorithm (FD-L algorithm) based on the lifted FD cuts. Finally, we report on computational results based on several test instances from the literature. The results are promising and show that both algorithms can outperform a standard direct solver and a disjunctive decomposition algorithm on some large-scale instances. Furthermore, the FD-L algorithm provides better performance than the FD algorithm in general.