数据包络分析
计算机科学
过程(计算)
反向
计量经济学
运筹学
经济
数学优化
数学
几何学
操作系统
作者
Mehdi Soltanifar,Mojtaba Ghiyasi,Hamid Sharafi
出处
期刊:Ima Journal of Management Mathematics
[Oxford University Press]
日期:2022-03-07
卷期号:34 (3): 491-510
被引量:2
标识
DOI:10.1093/imaman/dpac001
摘要
Abstract Data envelopment analysis (DEA) is a mathematical programming technique for efficiency analysis. For dealing with the data in ratio form, the DEA model for ratio data known as DEA-R exists in the literature. However, some ratio data like financial risk may be negative naturally. In this paper, we contribute to the literature in two ways. In the first place, we deal with DEA-R models in the presence of negative ratio data by proposing an inverse DEA model for merger analysis. In the second place, we develop DEA-R models for merger analysis that can deal with negative data. We apply our models in a real-world application of efficiency and merger analysis of an Iranian bank with 66 branches. The proposed models maintain data confidentiality. This motivates managers to participate in the evaluation and merger process. Our models also provide a reasonable endogenous weight restriction framework without restricting weights exogenously.
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