• DocumentCode
    506779
  • Title

    A hybrid intelligent algorithm for grain logistics vehicle routing problem

  • Author

    Jun, Jian ; Zhen, Tong ; Ge, Hongyi

  • Author_Institution
    Coll. of Inf. Sci. & Eng., Henan Univ. of Technol., Zhengzhou, China
  • Volume
    2
  • fYear
    2009
  • fDate
    20-22 Nov. 2009
  • Firstpage
    684
  • Lastpage
    687
  • Abstract
    Grain logistics vehicle routing problem is derived from vehicle routing problem, they have been a focus of research in the grain logistics managements recently, which the aim is to use the limited vehicles to a large number of jobs so that the maximum number of jobs can be completed with minimum cost. Aiming at the characteristics of the large batch and multi-point to multi-point transportation of grain logistics, a hybrid particle swarm optimization (PSO) with simulated annealing (SA) algorithm is proposed to solve grain logistics vehicle routing problem (VRP) in this paper. The experimental results manifest that the hybrid algorithm of PSO can solve grain logistics VRPTM quickly, the proposed algorithm is effective, and can reduce the cost of distribution.
  • Keywords
    agricultural products; logistics; particle swarm optimisation; simulated annealing; transportation; grain logistics managements; grain logistics vehicle routing problem; hybrid intelligent algorithm; hybrid particle swarm optimization; simulated annealing algorithm; Automotive engineering; Costs; Educational institutions; Information science; Intelligent vehicles; Logistics; Particle swarm optimization; Routing; Simulated annealing; Technology management; Vehicle Routing Problem; grain Logistics; hybrid particle swarm optimization; simulating annealing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-4754-1
  • Electronic_ISBN
    978-1-4244-4738-1
  • Type

    conf

  • DOI
    10.1109/ICICISYS.2009.5358307
  • Filename
    5358307