Title: An optimisation method of factory terminal logistics distribution route based on K-means clustering

Authors: Hui Zhang

Addresses: Shandong Polytechnic, Management Institute, Jinan Shandong 253100, China

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.

Keywords: k-means clustering algorithm; factory end logistics; logistics distribution route; location of distribution centre.

DOI: 10.1504/IJMTM.2023.131305

International Journal of Manufacturing Technology and Management, 2023 Vol.37 No.2, pp.184 - 198

Received: 18 Sep 2021
Received in revised form: 02 Nov 2021
Accepted: 13 Apr 2022
Published online: 06 Jun 2023 *

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