AP clustering algorithm for analysis of the impact of cold chain distribution center location on logistics costs

冷链 物流中心 计算机科学 聚类分析 配送中心 熵(时间箭头) 供应链 选择(遗传算法) 经销商 数据挖掘 算法 运筹学 业务 数学 人工智能 工程类 机械工程 物理 量子力学 商业 营销
作者
Kun He
出处
期刊:Journal of Industrial and Production Engineering [Taylor & Francis]
卷期号:40 (8): 661-676 被引量:5
标识
DOI:10.1080/21681015.2023.2257211
摘要

ABSTRACTThe distribution center is a transit place for goods in the logistics network, used to achieve the distribution of goods. Compared with ordinary logistics, cold chain logistics has higher requirements for timeliness due to the low temperature or ultra-low temperature requirements of transport objects. Aiming at the problems of high cost and low efficiency of cold chain distribution center location, a new location model of cold chain distribution center is developed. The Affinity Propagation (AP) clustering algorithm is used to simplify location selection. And combine the binary semantics with entropy to reduce the subjectivity of the binary semantics in the process of experiment. The results show that the location selection using the research method is optimal, and the problem that multiple secondary distribution centers are the same retailer will not appear. The research method is more objective and scientific for the location of cold chain distribution centers.Compared with ordinary logistics, cold chain logistics has higher requirements for timeliness. Distribution center is the transfer place of goods in the logistics network, which plays an important role in the logistics supply chain system. A new location model of cold chain distribution center is developed. Using Affinity Propagation (AP) clustering algorithm to simplify location selection and combining binary semantics with entropy method can further improve the objectivity of influencing factor index weights. The results show that the problem of multiple secondary distribution centers providing logistics services for the same retailer will not occur in the research method, and the location selection results are optimal. And the logistics cost can be reduced by 0.042%. This study improves the distribution efficiency, enhances the customer experience of cold chain logistics distribution, and provides certain technology and reference value for the development of cold chain logistics distribution.KEYWORDS: AP clustering algorithmcostdistribution centercold chain logisticssite selection Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsKun HeKun He, a teacher at Chuzhou Polytechnic, specializing in the field of economics.
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