@Article{jai.2021.016565,
AUTHOR = {Yizhi Liu, Rutian Qing, Liangran Wu, Min Liu, Zhuhua Liao, Yijiang Zhao},
TITLE = {Exploring Hybrid Genetic Algorithm Based Large-Scale Logistics  Distribution for BBG Supermarket},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {33--43},
URL = {http://www.techscience.com/jai/v3n1/42099},
ISSN = {2579-003X},
ABSTRACT = {In the large-scale logistics distribution of single logistic center, the 
method based on traditional genetic algorithm is slow in evolution and easy to 
fall into the local optimal solution. Addressing at this issue, we propose a novel 
approach of exploring hybrid genetic algorithm based large-scale logistic 
distribution for BBG supermarket. We integrate greedy algorithm and hillclimbing algorithm into genetic algorithm. Greedy algorithm is applied to 
initialize the population, and then hill-climbing algorithm is used to optimize 
individuals in each generation after selection, crossover and mutation. Our 
approach is evaluated on the dataset of BBG Supermarket which is one of the top 
10 supermarkets in China. Experimental results show that our method 
outperforms some other methods in the field.},
DOI = {10.32604/jai.2021.016565}
}