Optimal design of transportation distance in logistics supply chain model based on data mining algorithm
Supply chain management first needs to solve the location problem of distribution center of logistics. At present, the application of the centre-of-gravity method to calculate site selection is relative extensive. Based on the method of extreme distance calculation in the field of data mining technology, a new supply chain distribution center selection method combined with clustering algorithm and the centre-of-gravity selection method was proposed in this paper. In order to avoid the limitation of the traditional centre-of-gravity algorithm, the geographical location price was added to the optimization algorithm as the weight value, and the total cost of the model was calculated; then a “three-segment” data mining clustering algorithm was given to improve the efficiency of clustering calculation and avoid isolation finally, the K-means algorithm, the optimized three-segment algorithm and the hierarchical clustering algorithm and so on were compared, and the simulation calculation was carried out. It can be found that the algorithm of clustering center of gravity of the extreme distance data mining can reduce the cost and is advantageous to solve the problem of the location of the supply chain logistics center of gravity location.
KeywordsData mining Supply chain Clustering algorithm Logistics distribution center
This paper is supported by Jiangsu Key Construction disciplines project for applied economy.
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