摘要
针对工程机械备件需求的随机性、多样性及分类指标复杂等特点提出一个两阶段分类方法。第一阶段根据工程机械备件需求时间序列的平稳性把备件分两类;第二阶段综合影响备件分类的价值、服务及时间等因素,将粗糙集理论(Rough Set,RS)与自组织映射(Self-Organizing Map,SOM)神经网络相结合,设计RS-SOM聚类模型。先用模糊C均值聚类算法对指标数据进行离散化处理,再用改进的分明矩阵算法对指标集进行降维处理,在基于核的SOM模型中,通过引入粗糙集理论的上、下近似集来改进SOM训练过程,最后得到工程机械备件的聚类结果。数据实验证明,与ABC分类法和传统的SOM聚类方法相比,该方法能综合考虑各种影响因素对备件分类的影响,并能明确区分需求变动趋势不同的备件,为工程机械备件需求预测和库存控制提供可靠的依据。
A two-stage classification method is proposed for engineering machinery spare parts aiming at the characteristics of ran- domness, diversity and complexity. In the first stage, the spare parts are divided into two categories according to the stability of the service spare demand time series. In the second stage, combined with the factors such as the value, service, time and other factors of the spare parts classification,the Rough Set （RS） theory and Self-Organizing Map （SOM） neural network are combined to de- sign the RS-SOM clustering model. The index data are discretized by using fuzzy c-means clustering algorithm. Then the improved matrix algorithm is used to reduce the index set. In the kernel based SOM model,the training process is improved by introducing rough set theory. Finally, the clustering results of engineering machinery spare parts are obtained. Data experiments show that compared with the method of the ABC classification method and the traditional SOM clustering method, the classification result is better and it can provide a more reliable basis for the selection of the spare parts forecasting method and inventory strategy.
出处
《现代制造工程》
CSCD
北大核心
2017年第6期37-44,共8页
Modern Manufacturing Engineering
基金
国家自然科学基金资助项目(71271220)
广西高校人文社会科学重点研究基地基金资助项目(QN001)
关键词
工程机械备件
两阶段分类法
需求时间序列
粗糙集
自组织映射神经网络
engineering machinery spare parts
two-stage classification method
requirement time series
rough set
self organizing map neural network
