Preprocessing in Stochastic Programming: The Case of Linear Programs

The purpose of this paper is to investigate different preprocessing procedures for stochastic linear programs. The procedures are meant to be of use mostly for the modeler, but there are important computational implications as well. We discuss how to check if the problem under investigation is overspecified, i.e have options (columns) that can never be in an optimal solution, if the problem is tight or loose in terms of feasibility, and we demonstrate how to achieve relatively complete recourse in a number of cases. Computational results are detailed. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.