Title :
Application of crossover mutation ant colony algorithm in emergency logistics vehicle routing problem
Author :
Sun, Yunshan ; Zhang, Liyi ; Fei, Teng ; Li, Yanqin
Author_Institution :
Dept. of Inf. Eng., Tianjin Univ. of Commerce, Tianjin, China
Abstract :
Ant colony algorithm is a kind of novel simulation-biological evolution algorithm. An improved ant colony algorithm was utilized to solve the vehicle routing problem in emergency logistics. Genetic algorithm was utilized to optimize the parameters of ant colony algorithm. The algorithm possesses some characteristics such as strong total researching ability. The experimental results show that the improved ant colony algorithm possesses better optimization quantity and effect than the traditional ant colony algorithm.
Keywords :
ant colony optimisation; cost reduction; emergency services; genetic algorithms; logistics; transportation; vehicles; cost savings; crossover mutation ant colony algorithm; emergency logistics vehicle routing problem; genetic algorithm; parameter optimization; simulation-biological evolution algorithm; Equations; Genetic algorithms; Logistics; Mathematical model; Optimization; Routing; Vehicles; cross mutation ant colony algorithm; emergencylogistics; routing optimization; vehicle routing problem;
Conference_Titel :
Systems and Informatics (ICSAI), 2012 International Conference on
Conference_Location :
Yantai
Print_ISBN :
978-1-4673-0198-5
DOI :
10.1109/ICSAI.2012.6223149
