Forecast of Spare Parts Inventory Risk Level Based on Support Vector Machine
This article presents a new classification approach to inventory risk level of spare parts which based on the support vector machine classification principle. First, a fuzzy evaluation of spare parts is made in terms of their availability of suppliers, importance, predictability of failure, specificity and lead time. Then a one versus one classification machine model is established. Choosing a sample of historical data of spare parts and undertaking an OVO training stimulation. The model is used to predict the inventory risk levels of test data. The result in this experiment indicates that it is feasible to apply the support vector machine to forecast the spare parts inventory risk level
spare parts risk level support vector machine
Xiang-yu SU Xiao-lin ZHOU Yan MO
College of Economic and Management,ZheJiang Sci-Tech University,HangZhou,P.R.China
国际会议
厦门
英文
1344-1346
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）