基于模糊软集合和Bayes的后续备件组合预测方法

针对后续备件需求预测精度低的问题,提出一种基于模糊软集合和贝叶斯的后续备件组合预测方法。首先根据后续备件的消耗规律,分别提出后续备件因果预测模型和时序预测模型;然后选取残差平方和、信息熵和相关系数作为预测效果的评价准则,以两种单项预测方法的预测效果作为先验信息,采用德尔菲法对单项预测方法进行模糊评价,构建模糊软映射;最后综合先验信息与专家模糊评价值,利用贝叶斯方法确定组合权系数求得预测结果。结合算例,对比分析方法的优越性,表明该方法具有较好的合理性和有效性。

Combined Forecasting Approach for Subsequent Spare Parts Based on Fuzzy Soft Set and Bayesian

In order to solve the problem of low accuracy of the demand prediction for the subsequent spare parts, a new method for the prediction of subsequent spare parts is proposed by using fuzzy soft set and Bayesian. Based on the consumption rule of the subsequent spare parts, the causal prediction model and the time series prediction model are presented, and the sum of squared residuals, the information entropy and correlation coefficient are chosen as the evaluation criterion of prediction error. The prediction effect of two single prediction methods is used as prior information. Delphi method is used to evaluate the single prediction method and construct fuzzy soft mapping. Finally, combining the prior information and the fuzzy evaluation value of the experts, the combination weight coefficient is determined by Bayesian method. An example is included to show the superiority, rationality and validity of the proposed method.