摘要
本文首先介绍了前人建立的模糊DEA模型,考虑这些模型存在人为提高效率值、未充分利用模糊信息、计算量过大的问题,建立了一个新的L-R DEA模型,该模型基于α-截集的模糊数变换,将指标集分解为多个指标子集,分别对每个子集进行效率计算,最后应用熵值法确定决策单元(DMU)的最终效率值。该方法可以对DMU进行充分排序,扩大了DEA的应用领域。文末通过一算例来说明新模型的有效性。
This paper first introduces the fuzzy DEA model which is created by predecessors. Considering the problems of increasing efficiency value, do not make full use of the fuzzy information and having large amount of calculation in these models, this paper sets up a new L-R DEA model of fuzzy transformation based on a-cut sets. The set is decomposed into several subsets indicators and efficiency is calculated separately for each subset, finally it uses entropy method to determine the final efficiency values of decision-making units. A numerical example is given to illustrate the effectiveness of the new model
出处
《价值工程》
2013年第29期121-123,共3页
Value Engineering
关键词
模糊DEA
截集
区间效率
指标子集
熵权
fuzzy DEA
cut sets
interval efficiency
index subset
entropy weight
