Solving chance-constrained combinatorial problems to optimality

Solving chance-constrained combinatorial problems to optimality

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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:
Abstract:

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.

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