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
Link To Document :
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