• DocumentCode
    507843
  • Title

    Vehicle Routing Problem with Time Windows: A Hybrid Particle Swarm Optimization Approach

  • Author

    Liu, Xiaoxiang ; Jiang, Weigang ; Xie, Jianwen

  • Author_Institution
    Dept. of Comput. Sci., Jinan Univ., Zhuhai, China
  • Volume
    4
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    502
  • Lastpage
    506
  • Abstract
    Vehicle routing problem (VRP) is a well-known combinatorial optimization and nonlinear programming problem seeking to service a number of customers with a fleet of vehicles. This paper proposes a hybrid particle swarm optimization (HPSO) algorithm for VRP. The proposed algorithm utilizes the crossover operation that originally appears in genetic algorithm (GA) to make its manipulation more readily and avoid being trapped in local optimum, and simultaneously for improving the convergence speed of the algorithm, level set theory is also added to it. We employ the HPSO algorithm to an example of VRP, and compare its result with those generated by PSO, GA, and parallel PSO algorithms. The experimental comparison results indicate that the performance of HPSO algorithm is superior to others, and it will become an effective approach for solving discrete combinatory problems.
  • Keywords
    genetic algorithms; nonlinear programming; particle swarm optimisation; set theory; transportation; algorithm convergence speed; combinatorial optimization; genetic algorithm; hybrid particle swarm optimization approach; level set theory; nonlinear programming problem; time windows; vehicle routing problem; Computer science; Convergence; Educational institutions; Genetic algorithms; Level set; Mathematical model; Particle swarm optimization; Quality of service; Routing; Vehicles; Particle Swarm Optimization; Vehicle Routing Problem;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2009. ICNC '09. Fifth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3736-8
  • Type

    conf

  • DOI
    10.1109/ICNC.2009.353
  • Filename
    5363419