DocumentCode
2539711
Title
Mixed Climbing Particle Swarm Algorithm in the VRP
Author
Binda Wang ; Kejing Wang ; Fuguang Bao ; Lei Zhang ; Li Shen
Author_Institution
Sch. of Comput. Sci. & Inf. Eng., Zhejiang Gongshang Univ., Hang Zhou, China
fYear
2012
fDate
12-14 Oct. 2012
Firstpage
554
Lastpage
557
Abstract
In the vehicle routing problem (VRP), it is usually difficult to confederate the cargo and arrange vehicles path. Due to performance reasons, the traditional shortest path algorithm can not be applied to the large scale of VRP. On the basis of the VRP mathematical model, this paper constructs a mixed climbing particle swarm algorithms to solve the problem. First, through coding, the VRP problem is divided into two sub-problems: task allocation and single vehicle path optimization. Particle swarm algorithm is in charge of controlling the overall situation and allocating task, while hill-climbing algorithm is responsible for calculating the vehicle path optimization (fitness). Finally, by performing experiments in MATLAB programming and comparison of the operational results of the matrix method and genetic algorithm, the algorithm is shown to be feasible in solving VRP and have higher practicability.
Keywords
goods distribution; logistics; particle swarm optimisation; vehicle routing; MATLAB programming; VRP mathematical model; cargo; hill-climbing algorithm; mixed climbing particle swarm algorithm; mixed climbing particle swarm algorithms; performance reasons; single vehicle path optimization; task allocation; vehicle path optimization; vehicle routing problem; Equations; Logistics; Mathematical model; Optimization; Particle swarm optimization; Resource management; Vehicles; VRP; cycling path optimization; mixed climbing particle swarm algorithm; task allocation;
fLanguage
English
Publisher
ieee
Conference_Titel
Business Computing and Global Informatization (BCGIN), 2012 Second International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4673-4469-2
Type
conf
DOI
10.1109/BCGIN.2012.150
Filename
6382592
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