• DocumentCode
    2907457
  • Title

    A Particle Swarm Optimization Algorithm for the Open Vehicle Routing Problem

  • Author

    Zhen, Tong ; Zhu, Yuhua ; Zhang, Qiuwen

  • Author_Institution
    Dept. of Control Sci. & Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
  • Volume
    2
  • fYear
    2009
  • fDate
    4-5 July 2009
  • Firstpage
    560
  • Lastpage
    563
  • Abstract
    In the open vehicle routing problem (OVRP), a vehicle does not return to the depot after servicing the last customer on a route. The description of this variant of the standard vehicle routing problem appeared in the literature over 20 years ago, but it has still received little attention from researchers for a satisfactory solution. In this paper, a novel real number encoding method of particle swarm optimization (PSO) for open vehicle routing problem is proposed. The vehicle is mapped into the integer part of the real number; and the sequence of customers in the vehicle is mapped into the decimal fraction of the real number. They are used to optimize the inner or outer routes and modify illegal solutions. In the experiments, a number of numerical examples are carried out for testing and verification. The Computational results confirm the efficiency of the proposed methodology.
  • Keywords
    encoding; number theory; particle swarm optimisation; transportation; vehicles; OVRP; customer sequence; decimal fraction; open vehicle routing problem; particle swarm optimization algorithm; real number encoding method; Ant colony optimization; Automotive engineering; Educational institutions; Genetic algorithms; Genetic engineering; Information science; Logistics; Particle swarm optimization; Routing; Vehicle driving; Efficiency and reliability; Open Vehicle Routing Problem (OVRP); Particle Swarm Optimization (PSO);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Environmental Science and Information Application Technology, 2009. ESIAT 2009. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3682-8
  • Type

    conf

  • DOI
    10.1109/ESIAT.2009.273
  • Filename
    5199954