Interval Efficiency of Two-stage Network DEA Model with Imprecise Data
Interval Efficiency of Two-stage Network DEA Model with Imprecise Data
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DOI：
10.3138/infor.51.3.142
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
Weiwei Zhu;Zhixiang Zhou
中科院分区：
文献类型：
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作者：
Weiwei Zhu;Zhixiang Zhou
摘要 传统的数据包络分析（DEA）模型假设输入和输出的数据是准确已知的。然而，输入和输出可以是序数关系，也可以是有界数据，也可以是模糊数据，这在DEA方法下进行了研究，利用模糊计算区间效率结果。本文研究了将不精确数据纳入两阶段网络DEA模型时的情况，并构建新模型以获得区间效率得分的下限和上限。与基于模糊数字的模型不同，我们提出的方法基于类似 DEA 的线性模型。本文表明，DEA最优结果在输入和输出边界处总是能够得到，而在中间变量的上键或下键处则不能得到最优结果。我们应用所提出的模型来评估台湾的一组人寿保险公司，以说明我们的模型的用途及其结果与现有基于模糊数的模型的差异。
Abstract The conventional data envelopment analysis (DEA) models assume that the data of inputs and outputs are exactly known. However, inputs and outputs can be in ordinal relations or bounded data, or fuzzy data, which have been studied under the DEA approach by using fuzzy for calculating interval efficiency results. The current paper studies the situation when imprecise data is incorporated into the two-stage network DEA model, and construct new models to obtain the lower and upper bounds of the interval efficiency scores. Unlike the fuzzy number-based models, our proposed approach is based on DEA-like linear models. This paper shows that DEA optimality results can always be achieved at the input and output bounds, while can not be achieved at the upper or lower bonds of intermediate variables. We apply the proposed models to evaluate a set of life insurance companies in Taiwan for illustrating the use of our models and the differences of its results from that of existing fuzzy number-based models.