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Bundle-based relaxation methods for multicommodity capacitated fixed charge network design
Discrete Applied Mathematics - Special issue on the combinatorial optimization symposium
A Dynamic Domain Contraction Algorithm for Nonconvex Piecewise Linear Network Flow Problems
Journal of Global Optimization
A Simplex-Based Tabu Search Method for Capacitated Network Design
INFORMS Journal on Computing
A solution approach to the fixed charge network flow problem using a dynamic slope scaling procedure
Operations Research Letters
A first multilevel cooperative algorithm for capacitated multicommodity network design
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
0-1 reformulations of the multicommodity capacitated network design problem
Discrete Applied Mathematics
Benders, metric and cutset inequalities for multicommodity capacitated network design
Computational Optimization and Applications
Large-Scale, Less-than-Truckload Service Network Design
Operations Research
A capacity scaling heuristic for the multicommodity capacitated network design problem
Journal of Computational and Applied Mathematics
A local branching heuristic for the capacitated fixed-charge network design problem
Computers and Operations Research
Combining Exact and Heuristic Approaches for the Capacitated Fixed-Charge Network Flow Problem
INFORMS Journal on Computing
Relax-and-cut for capacitated network design
ESA'05 Proceedings of the 13th annual European conference on Algorithms
International Journal of Applied Metaheuristic Computing
A cutting plane algorithm for the Capacitated Connected Facility Location Problem
Computational Optimization and Applications
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This paper describes a slope scaling heuristic for solving the multicomodity capacitated fixed-charge network design problem. The heuristic integrates a Lagrangean perturbation scheme and intensification/diversification mechanisms based on a long-term memory. Although the impact of the Lagrangean perturbation mechanism on the performance of the method is minor, the intensification/diversification components of the algorithm are essential for the approach to achieve good performance. The computational results on a large set of randomly generated instances from the literature show that the proposed method is competitive with the best known heuristic approaches for the problem. Moreover, it generally provides better solutions on larger, more difficult, instances.