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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.


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.


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.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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