Solving chance-constrained combinatorial problems to optimality

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...

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Vydáno v:Computational optimization and applications Ročník 45; číslo 3; s. 607 - 638
Hlavní autor: Klopfenstein, Olivier
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston Springer US 01.04.2010
Springer Nature B.V
Témata:
Algorithms
Approximation
Convex and Discrete Geometry
Integer programming
Linear programming
Management Science
Mathematics
Mathematics and Statistics
Operations Research
Operations Research/Decision Theory
Optimization
Probability
Random variables
Statistics
Studies
Integer linear programming
Chance-constrained programming
ISSN:0926-6003, 1573-2894
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Shrnutí: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.
Bibliografie:SourceType-Scholarly Journals-1
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ISSN:0926-6003
1573-2894
DOI:10.1007/s10589-008-9177-6