Title: Research and application on logistics distribution optimisation problem using big data analysis

Authors: Yu-ming Duan; Hai-tao Fu

Addresses: Chongqing City Management College, Chongqing 401331, China ' Chongqing City Management College, Chongqing 401331, China

Abstract: The logistics distribution contains wok with large data. To excavate valuable information from the data to improve the reduction and efficiency of logistics distribution cost, the optimisation of logistics distribution centre location is discussed under the environment of big data. The features of logistics distribution and algorithm design idea are provided using basic platform of MapReduce and integrated with data mining clustering algorithm. It is verified to provide a decision scheme for any logistics route optimisation in logistics distribution chain according to the size of the granularity of space division. Compared to traditional clustering, the computation overhead is much less that by Dijkstra distance, The mode using MapReduce to summarise the represents nodes performs parallel computing optimise the efficiency. The scheme also shows practicality and reliability to assist the logistics enterprises with lower cost and better benefit.

Keywords: logistics distribution; centre location; k-means; geodesic distance; big data.

DOI: 10.1504/IJICT.2019.102048

International Journal of Information and Communication Technology, 2019 Vol.15 No.1, pp.43 - 56

Received: 16 Nov 2017
Accepted: 24 Jan 2018

Published online: 03 Sep 2019 *

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