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A fuzzy mathematical programming approach to the assessment of efficiency with DEA models

机译：用DEA模型评估效率的模糊数学规划方法

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摘要

In many real applications, the data of production processes cannot be precisely measured. This is particularly worrying when assessing efficiency with frontier-type models, such as data envelopment analysis (DEA) models, since they are very sensitive to possible data errors. For this reason, the possibility of having available a methodology that allows the analyst to deal with imprecise data becomes an issue of great interest in these contexts. To that end, we develop some fuzzy versions of the classical DEA models (in particular, the BCC model) by using some ranking methods based on the comparison of α-cuts. The resulting auxiliary crisp problems can be solved by the usual DEA software. We show, in a numerical example, how our models become specially useful for detecting sensitive decision-making units. Our approaches can be seen as an extension of the DEA methodology that provides users and practitioners with models which represent some real life processes more appropriately.
机译：在许多实际应用中，无法精确测量生产过程的数据。当使用边界类型的模型（例如数据包络分析（DEA）模型）评估效率时，这尤其令人担忧，因为它们对可能的数据错误非常敏感。由于这个原因，在这些情况下，提供一种允许分析人员处理不精确数据的方法的可能性成为人们非常关注的问题。为此，我们使用一些基于α割比较的排序方法，开发了一些经典DEA模型的模糊版本（特别是BCC模型）。由此产生的辅助脆性问题可以通过常规的DEA软件解决。在一个数字示例中，我们展示了我们的模型如何对检测敏感的决策单位特别有用。我们的方法可以看作是DEA方法的扩展，它为用户和从业人员提供了更恰当地表示某些现实生活过程的模型。

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