基于数据挖掘技术的物流配送成本估计研究

Research on logistics distribution cost estimation based on data mining technology

传统物流配送成本估计方法对于配送费用数据的支持度阈值计算不够精确,导致物流配送成本估计困难,为此研究基于数据挖掘技术的物流配送成本估计方法。该方法通过聚类分析,将庞大的费用数据划分成具有相同特征的数据类簇,找出其中出现频繁的数据类簇计算每一特征属性下的支持度阈值,挖掘出数据之间的关联规则,利用回归差分移动平均法搭建数学模型,以此实现物流配送成本估计。实验结果表明,与传统成本估计方法相比,所研究的方法对于数据支持度阈值计算更加准确,挖掘到的关联规则更详尽,估计出的物流配送成本更加精确。由此可见,所研究的方法更适用于企业物流配送成本估计要求。

The traditional logistics distribution cost estimation method is not accurate enough to calculate the support threshold of the distribution cost data,which leads to the difficulty of logistics distribution cost estimation.Therefore,the logistics distribution cost estimation method based on data mining technology is studied.With this method,the huge cost data is divided into the data type of clusters with the same characteristics by means of cluster analysis,the data class clusters which appear frequently are found out to compute the support threshold of each feature attribute,the association rules between the data are mined,and the mathematical model is built with the regression difference moving average method.The logistics cost estimation method is realized in this way.The experimental results show that,in comparison with the traditional cost estimation method,the proposed method is more accurate in calculating the data support threshold,its mined association rules are more exhaustive and its estimated logistics distribution cost is more precise.It can be seen that the method is more suitable for cost estimation of logistics distribution enterprises.