• DocumentCode
    176519
  • Title

    Model and algorithm for the 4PL multi-objects routing optimization problem based on Low Carbon Logistics

  • Author

    Hu Guihong

  • Author_Institution
    Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
  • fYear
    2014
  • fDate
    May 31 2014-June 2 2014
  • Firstpage
    3235
  • Lastpage
    3238
  • Abstract
    With the development of Low Carbon Economy, Low Carbon Logistics has attracted more and more researches´ attention. Logistics routing optimization is one of the important ways to reduce carbon emissions. Based on the analysis of practical problem, considered the cost, time and carbon emissions three properties of the route, a mathematical model of the point to point multi-objects routing optimization of the Fourth Party Logistics (4PL) is established. Through using the method of weight sum that considered the customer´s behavioral factors to tackle the multi-objects problem. Considering the characteristics of the problem, a double selection strategy Ant Colony Optimization (DACO) algorithm is designed for the problem. The algorithm is tested by example analysis and numerical analysis with different parameter values and different scale of the problem. The results suggest that the algorithm is effective and efficient.
  • Keywords
    ant colony optimisation; logistics; search problems; 4PL multiobjects routing optimization problem; DACO algorithm; double selection strategy ant colony optimization; fourth party logistics; logistics routing optimization; low carbon logistics; Algorithm design and analysis; Carbon; Carbon dioxide; Companies; Logistics; Optimization; Routing; Fourth Party Logistics (4PL); Low Carbon Logistics; Multi-objects routing optimization problem; double selection strategy Ant Colony Optimization (DACO);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference (2014 CCDC), The 26th Chinese
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4799-3707-3
  • Type

    conf

  • DOI
    10.1109/CCDC.2014.6852732
  • Filename
    6852732