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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Vydáno v:	Mathematical programming computation Ročník 1; číslo 4; s. 249 - 293
Hlavní autoři:	Kılınç Karzan, Fatma, Nemhauser, George L., Savelsbergh, Martin W. P.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer-Verlag 01.12.2009
Témata:	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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Shrnutí:	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