DocumentCode
3057858
Title
Multiple vehicle routing with time windows using genetic algorithms
Author
Louis, Sushil J. ; Yin, Xiangying ; Yuan, Zhen Ya
Author_Institution
Dept. of Comput. Sci., Nevada Univ., Reno, NV, USA
Volume
3
fYear
1999
fDate
1999
Abstract
We use genetic algorithm to attack the vehicle routing problem with time windows. Previous work has shown that although merge crossover works better than traditional cross operators for this problem, it does poorly on problems with non-random customer locations. We modify the merge crossover operator to achieve better performance on problems with clustered customer locations. Our algorithm optimally solved three out of six benchmark problems and came within 0.23% of the optimal on the rest
Keywords
computational complexity; genetic algorithms; transportation; vehicles; benchmark problems; clustered customer locations; cross operators; genetic algorithms; merge crossover operator; multiple vehicle routing; non-random customer locations; time windows; vehicle routing problem; Biological cells; Computer science; Educational institutions; Genetic algorithms; Government; Job shop scheduling; Rail transportation; Routing; Vehicles; Waste materials;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Conference_Location
Washington, DC
Print_ISBN
0-7803-5536-9
Type
conf
DOI
10.1109/CEC.1999.785493
Filename
785493
Link To Document