Monotonicity of minimum distance inefficiency measures for Data Envelopment Analysis
Monotonicity of minimum distance inefficiency measures for Data Envelopment Analysis
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DOI：
10.1016/j.ejor.2016.12.028
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Kazutoshi Ando;Masato Minamide;Kazuyuki Sekitani;Jianming Shi
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文献类型：
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作者：
Kazutoshi Ando;Masato Minamide;Kazuyuki Sekitani;Jianming Shi
本研究探讨资料包络分析（DEA）模型之最小距离无效率测度.一个关键的问题是，这种措施不满足单调性，即，该度量可以向下级决策单元（DMU）提供比向上级决策单元更好的评估分数。为了克服这个问题，引入了一种称为扩展方面方法的变体。然而，这种方法需要满足一定的规律性条件。本文讨论了几类特殊的DEA模型，证明了对于这些模型，最小距离无效率测度满足单调性，而不需要正则性条件。此外，我们进行了计算实验，使用真实世界的数据集，从这些特殊的类，并证明了扩展的方面的方法可能高估的性能的DMU。
This research explores the minimum distance inefficiency measure for the Data Envelopment Analysis (DEA) model. A critical issue is that this measure does not satisfy monotonicity, i.e., the measure may provide a better evaluation score to an inferior decision making unit (DMU) than to a superior one. To overcome this, a variant called the extended facet approach has been introduced. This approach, however, requires a certain regularity condition to be met. We discuss several special classes of the DEA model, and show that for these models, the minimum distance inefficiency measure satisfies the monotonicity property without the regularity condition. Moreover, we conducted computational experiments using real-world data sets from these special classes, and demonstrated that the extended facet approach may overestimate the performance of a DMU.