Open Access

ARTICLE

Exploring Hybrid Genetic Algorithm Based Large-Scale Logistics Distribution for BBG Supermarket

by Yizhi Liu, Rutian Qing, Liangran Wu, Min Liu, Zhuhua Liao, Yijiang Zhao

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.

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Keywords

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