Multi-Criteria ABC Inventory Classification Model Based on Density Peak Clustering Method
For addressing the multi-criteria optimization problem of ABC inventory classification management,a variety of classification models have been proposed.Among them,the unsupervised machine learning method has attractive theoretical and practical characteristics,and its biggest advantage is that it does not involve subjectivity.Based on three inventory decision-making elements:average unit cost,annual currency usage and lead time,a multi-criteria ABC inventory classification model is proposed based on the density peak clustering method.This paper studies the performance and effectiveness of the comparison model by using the DPC model and the K-Means model,clusters and classifies the standard data set composed of 47 inventory units,classifies the DPC model,K-Means model and GMM using the cost-service performance method,and compares and analyzes the model classification results.The research results show that in terms of cost-service performance,the DPC model is superior to the K-Means model and the GMM model.At the same time,it is found that the classification results of these three unsupervised clustering models are better than several mathematical programming models using the same data set.
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