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

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. (C) 2008 Elsevier Ltd. All rights reserved.