A genetic clustering-based TCNN algorithm for capacity vehicle routing problem

  • Sun Huali*
  • , Xie Jianying
  • , Xue Yaofeng
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A novel genetic clustering-based transiently chaotic neural network (GCTCNN) algorithm for Capacity Vehicle Routing Problem (CVRP) is proposed. CVRP can be partitioned into two kinds of decisions: the selection of vehicles among the available vehicles and the routing of the selected fleet Using the clustering algorithm the customers are grouped into clusters and each cluster is served by one vehicle. Then transiently chaotic neural network solves the routes to optimality. Computation on benchmark problems and comparison with other known algorithm show that the proposed algorithm produces excellent solutions in short computing times.

Original languageEnglish
Title of host publicationProceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Pages262-265
Number of pages4
StatePublished - 2005
Externally publishedYes
Event2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05 - Beijing, China
Duration: 13 Oct 200515 Oct 2005

Publication series

NameProceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Volume1

Conference

Conference2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Country/TerritoryChina
CityBeijing
Period13/10/0515/10/05

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