摘要
面向物流需求的预测问题,提出了一种基于诱导有序加权平均算子和模糊层次分析法相结合的组合预测模型。首先,采用诱导有序加权平均算子建立组合预测模型,并基于历史数据解算各单项预测模型的加权系数;然后,针对组合预测模型中各单项模型的加权系数确定问题,引入模糊层次分析法,得到各单项预测模型的公平排序,进而确定各单项预测模型在预测年的诱导值序列,从而预测出相应物流需求量;最后通过应用算例验证了该模型的正确性和可靠性。
In this paper,we presented a combinatorial forecasting model based on induced ordered weighted averaging（IOWA） operator and fuzzy analytic hierarchy process（FAHP）： first of all,we set up the combinatorial forecasting model using IOWA operator and calculated the weighting coefficients of each single forecasting model based on historical data.Then,to obtain the weighted coefficient determination of the component model,we determined the fair ranking of each single forecasting model using FAHP as well as the induction value sequence of each model in the prediction year,and predicted the the corresponding logistics demand.Finally,we verified the correctness and reliability of the combination forecasting model by an application example.
出处
《物流技术》
北大核心
2012年第8期265-267,332,共4页
Logistics Technology
基金
湖南省教育厅资助项目(09c213)
关键词
组合预测
物流需求
诱导有序加权平均
模糊层次分析法
combinatorial forecasting
logistics demand
induced ordered weighted averaging
fuzzy analytic hierarchy process
