• DocumentCode
    238744
  • Title

    Variable neighborhood decomposition for Large Scale Capacitated Arc Routing Problem

  • Author

    Yi Mei ; Xiaodong Li ; Xin Yao

  • Author_Institution
    Sch. of Comput. Sci. & Inf. Technol., RMIT Univ., Melbourne, VIC, Australia
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    1313
  • Lastpage
    1320
  • Abstract
    In this paper, a Variable Neighborhood Decomposition (VND) is proposed for Large Scale Capacitated Arc Routing Problems (LSCARP). The VND employs the Route Distance Grouping (RDG) scheme, which is a competitive decomposition scheme for LSCARP, and generates different neighborhood structures with different tradeoffs between exploration and exploitation. The search first uses a neighborhood structure that is considered to be the most promising, and then broadens the neighborhood gradually as it is getting stuck in a local optimum. The experimental studies show that the VND performed better than the state-of-the-art RDG-MAENS counterpart, and the improvement is more significant when the subcomponent size is smaller. This implies a great potential of combining the VND with small subcomponents.
  • Keywords
    combinatorial mathematics; optimisation; vehicle routing; LSCARP; VND; combinatorial optimization problem; large scale capacitated arc routing problem; route distance grouping scheme; variable neighborhood decomposition; Benchmark testing; Computer science; Educational institutions; Electronic mail; Optimization; Routing; Vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2014 IEEE Congress on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-6626-4
  • Type

    conf

  • DOI
    10.1109/CEC.2014.6900305
  • Filename
    6900305