The Least-distance DEA Based Efficiency Improvement Under Multiple Perspectives
The Least-distance DEA Based Efficiency Improvement Under Multiple Perspectives
复制标题
多视角下基于最小距离DEA的效率提升
DOI：
10.1109/ieem50564.2021.9672865
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Hasuike Takashi
中科院分区：
文献类型：
--
作者：
Wang Xu;Hasuike Takashi
数据包络分析（DEA）被广泛用于评估和提高具有多个输入和输出的决策单元（dmu）的相对效率。然而，传统的DEA模型只能处理单一视角。在本研究中，我们提出了一种基于最小距离DEA的多角度效率改进方法。现有研究采用纳什议价博弈理论，在多个视角下避免冲突，寻求效率提升的合理方向。由于最近有效目标的实用性，我们首先提出了一个包含NBG的最小距离DEA模型。通过数值实验，比较了本文方法与前人方法的性能。结果表明，本文提出的方法可以(1)从多个角度评估dmu的效率，(2)为被评估的dmu提供更容易实现的效率改进建议。因此，该方法在决策中具有显著的潜在适用性。
Data envelopment analysis (DEA) is widely used to evaluate and improve the relative efficiency of decision making units (DMUs), which have multiple inputs and outputs. However, traditional DEA models can only handle a single perspective. In this study, we propose a new approach for efficiency improvement under multiple perspectives based on the least-distance DEA. The Nash bargaining game (NBG) theory has been used in extant studies to avoid conflicts and obtain a rational direction of efficiency improvement under multiple perspectives. Because of the practicality of the closest efficient target, we first propose a least-distance DEA model incorporating NBG. A numerical experiment is conducted to compare the performance of our proposed approach with that of previous studies. The results reveal that our proposed approach can (1) evaluate the efficiency of DMUs under multiple perspectives, and (2) provide more easy-to-achieve efficiency improvement suggestions for the assessed DMUs. Thus, the proposed approach has remarkable potential applicability in decision making.