Networks and Spatial Economics

, Volume 16, Issue 2, pp 447–468 | Cite as

A Genetic Algorithm Based on Relaxation Induced Neighborhood Search in a Local Branching Framework for Capacitated Multicommodity Network Design

  • Mohsen Momeni
  • Mohammadreza Sarmadi


The fixed-charge Capacitated Multicommodity Network Design (CMND) is a well-known problem of both practical and theoretical significance. This article proposes the Genetic Algorithm (GA) cooperative Relaxation Induced Neighborhood Search (RINS) in a Local Branching (LB) framework for CMND problem. GA algorithm is started by initial population which is made by two parents obtain from hybrid LB and RINS algorithms. The basic idea of the proposed solution method is to use the GA algorithm to explore the search space and the hybrid LB and RINS methods to move from current solution to neighbor solution. Adapting the metaheuristic algorithm with RINS method to fit within an LB framework represents an interesting challenge. To evaluate the proposed algorithm, the standard problems with different sizes are used. The parameters of the algorithm are tuned by design of experiments. In order to prove the efficiency and effectiveness of the proposed algorithm, the results are compared with the best results available in the literature. The statistical analysis shows high performance of the proposed algorithm.


Capacitated multicommodity network design problem Hybrid algorithms Local branching Relaxation induced neighborhood search Genetic algorithm 


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Rail Transportation DepartmentIran University of Science and TechnologyTehranIran

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