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

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  • Hui Zhang

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.

Suggested Citation

  • Hui Zhang, 2023. "An optimisation method of factory terminal logistics distribution route based on K-means clustering," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 37(2), pages 184-198.
    • Handle: RePEc:ids:ijmtma:v:37:y:2023:i:2:p:184-198
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