Steiner's problem in graphs: heuristic methods
Discrete Applied Mathematics - Special issue: combinatorial methods in VLSI
Provisioning a virtual private network: a network design problem for multicommodity flow
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Dynamic Programming and Strong Bounds for the 0-1 Knapsack Problem
Management Science
Building Steiner trees with incomplete global knowledge
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
SAINT '08 Proceedings of the 2008 International Symposium on Applications and the Internet
A GRASP Algorithm for the Connected Facility Location Problem
SAINT '08 Proceedings of the 2008 International Symposium on Applications and the Internet
A hybrid VNS for connected facility location
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
Connected facility location via random facility sampling and core detouring
Journal of Computer and System Sciences
MIP models for connected facility location: A theoretical and computational study
Computers and Operations Research
Dual-Based Local Search for the Connected Facility Location and Related Problems
INFORMS Journal on Computing
Branch-and-Cut-and-Price for Capacitated Connected Facility Location
Journal of Mathematical Modelling and Algorithms
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The Connected Facility Location problem combining facility location and Steiner trees has recently gained stronger scientific interest as it can be used to model the extension of last mile communication networks in so-called fiber-to-the-curb scenarios. We consider a generalization of this problem which considers capacity constraints on potential facilities and aims at maximizing the resulting profit by potentially supplying only a subset of all customers. In this work, we discuss two metaheuristic approaches for this problem based on variable neighborhood search and greedy randomized adaptive search. Computational results show that both approaches allow for computing high quality solutions in relatively short time.