Article: The application of big data analysis in logistics supply chain optimisation Journal: International Journal of Information and Communication Technology (IJICT) 2024 Vol.25 No.9 pp.104 - 116 Abstract: This article studies the application of big data in logistics supply chain optimisation, combining modern data processing technology with intelligent optimisation algorithms, aiming to improve supply chain efficiency and reduce logistics costs. The K-means clustering algorithm is used to partition the delivery area and customer demand, in order to optimise the allocation of logistics resources and warehouse layout, and reduce the redundancy of transportation paths. Next, based on the clustering results, the ant colony algorithm is utilised to address the optimisation of vehicle routing problem (VRP), finding the shortest path between multiple delivery points to minimise transportation time and cost. This article utilises the big data analysis platform Hadoop for data storage and processing, ensuring the efficient operation of algorithms on large-scale data. The results show that the supply chain optimisation strategy combining big data analysis, K-means clustering, and ant colony optimisation can improve delivery efficiency and reduce costs. Inderscience Publishers - linking academia, business and industry through research

Title: The application of big data analysis in logistics supply chain optimisation

Authors: Chunsheng Liu

Addresses: Jiangxi Institute of Fashion Technology, Nanchang 330201, China

Abstract: This article studies the application of big data in logistics supply chain optimisation, combining modern data processing technology with intelligent optimisation algorithms, aiming to improve supply chain efficiency and reduce logistics costs. The K-means clustering algorithm is used to partition the delivery area and customer demand, in order to optimise the allocation of logistics resources and warehouse layout, and reduce the redundancy of transportation paths. Next, based on the clustering results, the ant colony algorithm is utilised to address the optimisation of vehicle routing problem (VRP), finding the shortest path between multiple delivery points to minimise transportation time and cost. This article utilises the big data analysis platform Hadoop for data storage and processing, ensuring the efficient operation of algorithms on large-scale data. The results show that the supply chain optimisation strategy combining big data analysis, K-means clustering, and ant colony optimisation can improve delivery efficiency and reduce costs.

Keywords: big data; ant colony algorithm; ACO; K-means; logistics chain; vehicle routing problem; VRP.

DOI: 10.1504/IJICT.2024.143330

International Journal of Information and Communication Technology, 2024 Vol.25 No.9, pp.104 - 116

Received: 27 Sep 2024
Accepted: 11 Oct 2024
Published online: 13 Dec 2024 *

Title: The application of big data analysis in logistics supply chain optimisation

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