Variable Neighborhood Decomposition for Large Scale Capacitated Arc Routing Problem

被引:0
|
作者
Mei, Yi [1 ]
Li, Xiaodong [1 ]
Yao, Xin [2 ]
机构
[1] RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic 3000, Australia
[2] Univ Birmingham, Sch Comp Sci, CERCIA, Birmingham B15 2TT, W Midlands, England
关键词
TABU SEARCH ALGORITHM; INTERMEDIATE FACILITIES; BOUNDS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:1313 / 1320
页数:8
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