Information-based branching schemes for binary linear mixed integer problems

Branching variable selection can greatly affect the effectiveness and efficiency of a branch-and-bound algorithm. Traditional approaches to branching variable selection rely on estimating the effect of the candidate variables on the objective function. We propose an approach which is empowered by ex...

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Veröffentlicht in:Mathematical programming computation Jg. 1; H. 4; S. 249 - 293
Hauptverfasser: Kılınç Karzan, Fatma, Nemhauser, George L., Savelsbergh, Martin W. P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer-Verlag 01.12.2009
Schlagworte:
Full Length Paper
Mathematics
Mathematics and Statistics
Mathematics of Computing
Operations Research/Decision Theory
Optimization
Theory of Computation
90B40 Search theory
Branch-and-bound
90C11 Mixed integer programming
Computational algorithms
90-04 Explicit machine computation and programs (not the theory of computation or programming)
90-08 Computational methods
Variable selection
ISSN:1867-2949, 1867-2957
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Zusammenfassung:Branching variable selection can greatly affect the effectiveness and efficiency of a branch-and-bound algorithm. Traditional approaches to branching variable selection rely on estimating the effect of the candidate variables on the objective function. We propose an approach which is empowered by exploiting the information contained in a family of fathomed subproblems, collected beforehand from an incomplete branch-and-bound tree. In particular, we use this information to define new branching rules that reduce the risk of incurring inappropriate branchings. We provide computational results that demonstrate the effectiveness of the new branching rules on various benchmark instances.
ISSN:1867-2949
1867-2957
DOI:10.1007/s12532-009-0009-1