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Stabilizing branch‐and‐price for constrained tree problems
Author(s) -
Leitner Markus,
Ruthmair Mario,
Raidl Günther R.
Publication year - 2013
Publication title -
networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.977
H-Index - 64
eISSN - 1097-0037
pISSN - 0028-3045
DOI - 10.1002/net.21484
Subject(s) - column generation , mathematical optimization , branch and cut , steiner tree problem , computer science , variety (cybernetics) , tree (set theory) , path (computing) , piecewise linear function , class (philosophy) , mathematics , linear programming , mathematical analysis , geometry , artificial intelligence , programming language
Abstract We consider a rather generic class of network design problems in which a set or subset of given terminal nodes must be connected to a dedicated root node by simple paths and a variety of resource and/or quality of service constraints must be respected. These extensions of the classical Steiner tree problem on a graph can be well modeled by a path formulation in which individual variables are used for all feasible paths. To solve this formulation in practice, branch‐and‐price is used. It turns out, however, that a naive implementation of column generation suffers strongly from certain degeneracies of the pricing subproblem, leading to excessive running times. After analyzing these computational problems, we propose two methods to accelerate and stabilize column generation by using alternative dual‐optimal solutions. The resulting branch‐and‐price approach is practically tested on the rooted delay‐constrained Steiner tree problem and a quota‐constrained version of it. Results indicate that the proposed methods in general speed‐up the solution process dramatically, far more than a piecewise linear stabilization to which we compare. Furthermore, our branch‐and‐price approach exhibits on most test instances a better performance than a state‐of‐the‐art branch‐and‐cut approach based on layered graphs. As the new stabilization technique utilizing alternative dual‐optimal solutions is generic in the sense that it easily adapts to the inclusion of a large variety of further constraints and different objective functions, the proposed method is highly promising for a large class of network design problems. © 2012 Wiley Periodicals, Inc. NETWORKS, 2013

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