Open Access
ARTICLE
Exploring Hybrid Genetic Algorithm Based Large-Scale Logistics Distribution for BBG Supermarket
Yizhi Liu1,2, Rutian Qing1,2,*, Liangran Wu1,2, Min Liu1,2, Zhuhua Liao1,2, Yijiang Zhao1,2
1 Key Laboratory of Knowledge Processing and Networked Manufacturing, College of Hunan Province, Xiangtan, 411201, China
2 School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
* Corresponding Author: Rutian Qing. Email:
Journal on Artificial Intelligence 2021, 3(1), 33-43. https://doi.org/10.32604/jai.2021.016565
Received 05 January 2021; Accepted 19 March 2021; Issue published 02 April 2021
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.
Keywords
Cite This Article
Y. Liu, R. Qing, L. Wu, M. Liu, Z. Liao
et al., "Exploring hybrid genetic algorithm based large-scale logistics distribution for bbg supermarket,"
Journal on Artificial Intelligence, vol. 3, no.1, pp. 33–43, 2021.