A Dynamic Clustering Method to Large-Scale Distribution Problems

Tang Zhizhong (), Li Bo () and Qiu Hongyan ()
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Tang Zhizhong: College of Management and Economics, Tianjin University, Tianjin300072, China
Li Bo: College of Management and Economics, Tianjin University, Tianjin300072, China
Qiu Hongyan: College of Management and Economics, Tianjin University, Tianjin300072, China

Journal of Systems Science and Information, 2015, vol. 3, issue 1, 25-36

Abstract: This paper presents the dynamic fuzzy clustering method to solve the multi-producers to multi-customers large-scale distribution problem. The proposed method includes three phases: Static clustering, order processing, and dynamic clustering. Based on the distances among customers, k-means method is used to generate the static clusters. The service priorities of each producer serving the static customer groups are ranked according to the distance performance. In the case of fluctuant customer orders, order processing can divide customer orders into several consecutive periods. After the above two phases, the fuzzy clustering technique is applied to further conduct dynamic clustering based on the customer order attributes. Similarly, the service priorities of generated dynamic customer groups will be ranked according to the time attributes of orders. Finally, by the real case, the authors obtain the conclusion that using the proposed method, the total cost of the producer is reduced by about 35%, and the vehicle loading rates are almost above 95%.

Keywords: large-scale distribution scheduling; fluctuant orders; fuzzy clustering; dynamic groups (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:3:y:2015:i:1:p:25-36:n:3

DOI: 10.1515/JSSI-2015-0025

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