• DocumentCode
    2539704
  • Title

    Vehicle Routing Problem with Simultaneous Delivery and Pick-up Based on the Improved Genetic Algorithm

  • Author

    Zhu, Nuo ; Shao, Chunfu

  • Author_Institution
    MOE Key Lab. for Transp. Complex Syst. Theor. & Technol., Beijing Jiaotong Univ., Beijing, China
  • fYear
    2010
  • fDate
    13-15 Dec. 2010
  • Firstpage
    312
  • Lastpage
    316
  • Abstract
    Based on the traditional theory and algorithms of vehicle routing problem, the multi-objective VRPSDP mathematical model is established in considering the minimum of the number of vehicles and the transportation costs. The genetic algorithm is used as the solving algorithm of the model in this paper, in order to ensure the effectiveness of the chromosomes in the iterative process, the chromosome encoding and the genetic arithmetic operators, which are more suitable for solving the problem of the vehicle operating route are devised. Simulation experiments are done in the application of the modified Solomon R101 case, the optimal objective function value and the optimal program of arranging the vehicle routes are respectively obtained by the basic GA and the improved GA. According to the comparative analysis the convergence process of the optimal solution on two genetic algorithms, it is validated that the model is reasonable and the solving algorithm is effective.
  • Keywords
    genetic algorithms; transportation; vehicles; chromosome encoding; genetic arithmetic operators; improved genetic algorithm; iterative process; multi-objective VRPSDP mathematical model; vehicle routing problem; Biological cells; Encoding; Gallium; Mathematical model; Routing; Vehicles; Genetic Algorithm; Pick-up and Delivery; Transportation cost; Vehicle Routing Problem;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Genetic and Evolutionary Computing (ICGEC), 2010 Fourth International Conference on
  • Conference_Location
    Shenzhen
  • Print_ISBN
    978-1-4244-8891-9
  • Electronic_ISBN
    978-0-7695-4281-2
  • Type

    conf

  • DOI
    10.1109/ICGEC.2010.84
  • Filename
    5715432