• DocumentCode
    2539711
  • Title

    Mixed Climbing Particle Swarm Algorithm in the VRP

  • Author

    Binda Wang ; Kejing Wang ; Fuguang Bao ; Lei Zhang ; Li Shen

  • Author_Institution
    Sch. of Comput. Sci. & Inf. Eng., Zhejiang Gongshang Univ., Hang Zhou, China
  • fYear
    2012
  • fDate
    12-14 Oct. 2012
  • Firstpage
    554
  • Lastpage
    557
  • Abstract
    In the vehicle routing problem (VRP), it is usually difficult to confederate the cargo and arrange vehicles path. Due to performance reasons, the traditional shortest path algorithm can not be applied to the large scale of VRP. On the basis of the VRP mathematical model, this paper constructs a mixed climbing particle swarm algorithms to solve the problem. First, through coding, the VRP problem is divided into two sub-problems: task allocation and single vehicle path optimization. Particle swarm algorithm is in charge of controlling the overall situation and allocating task, while hill-climbing algorithm is responsible for calculating the vehicle path optimization (fitness). Finally, by performing experiments in MATLAB programming and comparison of the operational results of the matrix method and genetic algorithm, the algorithm is shown to be feasible in solving VRP and have higher practicability.
  • Keywords
    goods distribution; logistics; particle swarm optimisation; vehicle routing; MATLAB programming; VRP mathematical model; cargo; hill-climbing algorithm; mixed climbing particle swarm algorithm; mixed climbing particle swarm algorithms; performance reasons; single vehicle path optimization; task allocation; vehicle path optimization; vehicle routing problem; Equations; Logistics; Mathematical model; Optimization; Particle swarm optimization; Resource management; Vehicles; VRP; cycling path optimization; mixed climbing particle swarm algorithm; task allocation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Business Computing and Global Informatization (BCGIN), 2012 Second International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4673-4469-2
  • Type

    conf

  • DOI
    10.1109/BCGIN.2012.150
  • Filename
    6382592