Title :
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
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;
Conference_Titel :
Intelligent Systems (GCIS), 2010 Second WRI Global Congress on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-9247-3
DOI :
10.1109/GCIS.2010.171
