现代制造工程Issue(6)：37-44,8.DOI:10.16731/j.cnki.1671-3133.2017.06.007
工程机械备件需求特征分类模型
Demand characteristic classification model of engineering machinery spare parts
摘要
Abstract
A two-stage classification method is proposed for engineering machinery spare parts aiming at the characteristics of randomness,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 design 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.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Key words
engineering machinery spare parts/two-stage classification method/requirement time series/rough set/self organizing map neural network分类
信息技术与安全科学引用本文复制引用
罗薇,符卓..工程机械备件需求特征分类模型[J].现代制造工程,2017,(6):37-44,8.基金项目
国家自然科学基金资助项目(71271220) （71271220）
广西高校人文社会科学重点研究基地基金资助项目(QN001) （QN001）
