Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Li, Hua
Affiliations: School of Information Technology, Xichang University, Xichang, Sichuan 615000, China | E-mail: lihua1979xichang@163.com
Correspondence: [*] Corresponding author: School of Information Technology, Xichang University, Xichang, Sichuan 615000, China. E-mail: lihua1979xichang@163.com.
Abstract: In order to improve the centralized planning ability of logistics distribution path data, improve the efficiency of logistics distribution and reduce the cost of logistics distribution, this paper proposes an optimal path selection algorithm based on machine vision. Using machine vision technology to calibrate the coordinates of logistics distribution path, combined with EMD decomposition method and wavelet denoising method to remove redundant data in logistics distribution data, particle swarm optimization algorithm to complete logistics distribution path planning, and ant colony algorithm to realize the optimal path selection of logistics distribution. The experimental results show that the average distribution cost of this method is only 766.7 yuan, the distribution time is less than 0.3 h, and the customer satisfaction is as high as 98%, which shows that this method can effectively optimize the distribution path.
Keywords: Machine vision, logistics distribution, distribution path, optimal selection algorithm
DOI: 10.3233/JCM-226529
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 1, pp. 37-50, 2023
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl