A general algorithm for solving two-stage stochastic mixed 0–1 first-stage problems

Abstract

We present an algorithmic approach for solving large-scale two-stage stochastic problems having mixed 0–1 first stage variables. The constraints in the first stage of the deterministic equivalent model have 0–1 variables and continuous variables, while the constraints in the second stage have only continuous. The approach uses the twin node family concept within the algorithmic framework, the so-called branch-and-fix coordination, in order to satisfy the nonanticipativity constraints. At the same time we consider a scenario cluster Benders decomposition scheme for solving large-scale LP submodels given at each TNF integer set. Some computational results are presented to demonstrate the efficiency of the proposed approach.