Solving Vehicle Routing Problem with Time Windows with Hybrid Evolutionary Algorithm

Author

Mao, Yong ; Deng, Yanfang

Author_Institution

Dept. of Comput. Eng., Univ. of Electron. Sci. & Technol., Zhongshan, China

Volume

1

fYear

2010

fDate

16-17 Dec. 2010

Firstpage

335

Lastpage

339

Abstract

A new hybrid evolutionary algorithm, which based on genetic algorithm (GA), greedy randomized adaptive search procedure (GRASP), the expanding neighborhood search (ENS) strategy and particle swarm optimization (PSO), is introduced in order to solve vehicle routing problem with time windows (VRPTW). The work makes full use of the advantages of each algorithm. The computational experiments were carried out on typical Soomon benchmark problems. The results demonstrate that the proposed method is highly competitive, providing the best-known solutions to minimal distance.

Keywords

genetic algorithms; greedy algorithms; particle swarm optimisation; problem solving; transportation; Soomon benchmark problems; expanding neighborhood search; genetic algorithm; greedy randomized adaptive search procedure; hybrid evolutionary algorithm; particle swarm optimization; time windows; vehicle routing problem solving; Gallium; Operations research; Optimization; Particle swarm optimization; Routing; Search problems; Vehicles; genetic algorithm; particle swarm optimization; vehicle routing problem;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems (GCIS), 2010 Second WRI Global Congress on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-9247-3

Type

conf

DOI

10.1109/GCIS.2010.171

Filename

5708772