An optimisation method of factory terminal logistics distribution route based on K-means clustering
by Hui Zhang
International Journal of Manufacturing Technology and Management (IJMTM), Vol. 37, No. 2, 2023

Abstract: Aiming at the problems of scattered logistics data and low logistics distribution efficiency in the existing factory end logistics distribution route planning methods, a factory end logistics distribution route optimisation method based on K-means clustering is proposed. Firstly, information entropy is introduced to optimise the classical K-means dynamic clustering algorithm to collect the factory end logistics distribution data. Then, a priori clustering insertion algorithm is used to process the redundant data in the collected logistics distribution data. The priority characteristics of logistics distribution nodes and the subset of distribution service requirements are established and the end distribution route planning process is designed. Finally, by setting the starting point of collection and distribution route through the process, determine the data weight in the distribution dataset, the optimal route of factory end logistics distribution to realise optimisation. The results show that this method has low cost and time-consuming less than 0.3 h.

Online publication date: Tue, 06-Jun-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Manufacturing Technology and Management (IJMTM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com