On bridging the gap between stochastic integer programming and MIP solver technologies

Abstract

Stochastic integer programs (SIPs) represent a very difficult class of optimization problems arising from the presence of both uncertainty and discreteness in planning and decision problems. Although applications of SIPs are abundant, nothing is available by way of computational software. On the other hand, commercial software packages for solving deterministic integer programs have been around for quite a few years, and more recently, a package for solving stochastic linear programs has been released. In this paper, we describe how these software tools can be integrated and exploited for the effective solution of general-purpose SIPs. We demonstrate these ideas on four problem classes from the literature and show significant computational advantages.