Bundle-based relaxation methods for multicommodity capacitated fixed charge network design
Discrete Applied Mathematics - Special issue on the combinatorial optimization symposium
A Simplex-Based Tabu Search Method for Capacitated Network Design
INFORMS Journal on Computing
A first multilevel cooperative algorithm for capacitated multicommodity network design
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
Design and Analysis of Experiments
Design and Analysis of Experiments
A survey on benders decomposition applied to fixed-charge network design problems
Computers and Operations Research
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Tuning Metaheuristics: A Machine Learning Perspective
Tuning Metaheuristics: A Machine Learning Perspective
A local branching heuristic for the capacitated fixed-charge network design problem
Computers and Operations Research
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The network design is a well-known problem, both of practical and theoretical significance. Network design models are extensively used to represent a wide range of planning and operations management issues in transportation, telecommunications, logistics, production and distribution. This paper presents a solution method for node-arc formulation of capacitated fixed-charge multicommodity network design problems. The proposed method is a hybrid algorithm of Simplex method and simulated annealing metaheuristic. The basic idea of the proposed algorithm is to use a simulated annealing algorithm to explore the solution space, where the revised Simplex method is used to evaluate, select and implement the moves. In the proposed algorithm, the neighborhood structure is pivoting rules of the Simplex method that provide an efficient way to reach the neighbors of current solution. To evaluate the proposed algorithm, the standard problems with different sizes are used. The algorithm parameters are tuned by design of experiments approach and the most appropriate values for the parameters are adjusted. The performance of the proposed algorithm is evaluated by statistical analysis. The results show high efficiency and effectiveness of the proposed algorithm.