• DocumentCode
    1631790
  • Title

    Study on route optimization of logistics distribution based on ant colony and genetic algorithm

  • Author

    Qin, Yuquan ; Qin, Liyan ; Li, Haimin

  • Author_Institution
    Sch. of Manage. & Inf., Shandong Transp. Vocational Coll., Weifang, China
  • Volume
    1
  • fYear
    2012
  • Firstpage
    285
  • Lastpage
    288
  • Abstract
    Ant Colony Optimization (ACO) algorithm and genetic algorithms (GA) are two commonly used methods dealing with vehicle route optimizing. According to the characteristics of the two methods, by combining the two algorithms, a hybrid algorithm is proposed to solve the vehicle routing problem, avoiding the disadvantages of long time searching, easily falling into local optimal solution in ACO and the shortcomings of iterative redundancy, inefficiency in GA. Some experimental results prove that the hybrid optimization algorithm (HOA) is feasible and efficient in solving the problem of vehicle route optimization in logistics distribution.
  • Keywords
    ant colony optimisation; genetic algorithms; road vehicles; ant colony; genetic algorithm; hybrid optimization algorithm; iterative redundancy; local optimal solution; logistics distribution; long time searching; route optimization; Algorithm design and analysis; Cities and towns; Genetic algorithms; Logistics; Optimization; Routing; Vehicles; ant colony algorithm; genetic algorithm; hybrid optimization algorithm; logistics distribution; path optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Instrumentation & Measurement, Sensor Network and Automation (IMSNA), 2012 International Symposium on
  • Conference_Location
    Sanya
  • Print_ISBN
    978-1-4673-2465-6
  • Type

    conf

  • DOI
    10.1109/MSNA.2012.6324569
  • Filename
    6324569