Article ID: | iaor20103232 |
Volume: | 45 |
Issue: | 3 |
Start Page Number: | 607 |
End Page Number: | 638 |
Publication Date: | Apr 2010 |
Journal: | Computational Optimization and Applications |
Authors: | Klopfenstein Olivier |
The aim of this paper is to provide new efficient methods for solving general chance-constrained integer linear programs to optimality. Valid linear inequalities are given for these problems. They are proved to characterize properly the set of solutions. They are based on a specific scenario, whose definition impacts strongly on the quality of the linear relaxation built. A branch-and-cut algorithm is described to solve chance-constrained combinatorial problems to optimality. Numerical tests validate the theoretical analysis and illustrate the practical efficiency of the proposed approach.