Lagrangian Cardinality Cuts and Variable Fixing for Capacitated Network Design
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
An Augmented Lagrangian Algorithm for Large Scale Multicommodity Routing
Computational Optimization and Applications
Grasp Embedded Scatter Search for the Multicommodity Capacitated Network Design Problem
Journal of Heuristics
A first multilevel cooperative algorithm for capacitated multicommodity network design
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
Variable Disaggregation in Network Flow Problems with Piecewise Linear Costs
Operations Research
Lagrangean heuristic for primary routes assignment in survivable connection-oriented networks
Computational Optimization and Applications
AMCOS'05 Proceedings of the 4th WSEAS International Conference on Applied Mathematics and Computer Science
A heuristic to find a p%-robust network design
ISTASC'04 Proceedings of the 4th WSEAS International Conference on Systems Theory and Scientific Computation
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
Algorithms for the non-bifurcated network design problem
Journal of Heuristics
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
The k-Cardinality Tree Problem: Reformulations and Lagrangian Relaxation
Discrete Applied Mathematics
Alternating control tree search for knapsack/covering problems
Journal of Heuristics
Column Generation with Dynamic Duty Selection for Railway Crew Rescheduling
Transportation Science
LEGUP: using heterogeneity to reduce the cost of data center network upgrades
Proceedings of the 6th International COnference
Anycasting in connection-oriented computer networks: Models, algorithms and results
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
Relax-and-cut for capacitated network design
ESA'05 Proceedings of the 13th annual European conference on Algorithms
An iterated local search heuristic for a capacitated hub location problem
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Metric inequalities and the Network Loading Problem
Discrete Optimization
A cutting plane algorithm for the Capacitated Connected Facility Location Problem
Computational Optimization and Applications
Hi-index | 0.00 |
The capacitated network design problem is a multicommodity minimal cost network flow problem with fixed charges on the arcs and is well known to be NP-hard. The problem type is very common in the context of transportation networks, telecommunication networks, etc. In this paper we propose an efficient method for this problem, based on a Lagrangian heuristic within a branch-and-bound framework. The Lagrangian heuristic uses a Lagrangian relaxation to obtain easily solved subproblems and solves the Lagrangian dual by subgradient optimization. It also includes techniques for finding primal feasible solutions. The Lagrangian heuristic is then embedded into a branch-and-bound scheme that yields further primal improvements. Special penalty tests and cutting criteria are developed. The branch-and-bound scheme can either be an exact method that guarantees the optimal solution of the problem or be a quicker heuristic. The method has been tested on networks of various structures and sizes. Computational comparisons between this method and a state-of-the-art mixed-integer code are presented. The method is found to be capable of generating good feasible solutions to large-scale problems within reasonable time and data storage limits.