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Issue title: Special section: Artificial Intelligence driven Big Data Analytics for COVID-19
Guest editors: Xiaolong Li
Article type: Research Article
Authors: Yong, Changa; * | Lu, Yunb; c
Affiliations: [a] Vocational and Technical College, Anshan Normal University, Anshan, Liaoning, China | [b] School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China | [c] Marine Technology Society, Columbia, MD, USA
Correspondence: [*] Corresponding author. Chang Yong, Vocational and Technical College, Anshan Normal University, Anshan 114001, Liaoning, China. E-mail: [email protected].
Abstract: Under the influence of COVID-19, the efficiency of e-commerce distribution in public health emergencies has become the key to ensuring people’s normal lives. With the development of e-commerce, the location of distribution center is becoming more and more important, which is related to the healthy and long-term development of e-commerce. However, the traditional location efficiency is low, which cannot play an immediate role in the development of e-commerce. This paper combines weiszfeld algorithm, improves particle swarm optimization algorithm, improves the operation efficiency and location accuracy, and finally gets a relatively satisfactory result. Results data analysis shows that the improvement and integration of the algorithm is reasonable and effective. This method has important reference value for e-commerce location.
Keywords: E-commerce, center location, particle swarm optimization, Weiszfeld algorithm, COVID-19
DOI: 10.3233/JIFS-189275
Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8783-8793, 2020
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