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A sequence of targets toward a common best practice frontier in DEA

机译:针对DEA中常见的最佳实践前沿的一系列目标

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Original data envelopment analysis models treat decision-making units as independent entities. This feature of data envelopment analysis results in significant diversity in input and output weights, which is irrelevant and problematic from the managerial point of view. In this regard, several methodologies have been developed to measure the efficiency scores based on common weights. Specifically, Ruiz and Sirvant (Omega 65:1–9, 2016 ) formulated an aggregated DEA model to minimize the gap between actual performances and best practices and identify a common best practice frontier. Their model is capable of determining target units for all units under evaluation, simultaneously, with the property that all of them are located on a common best practice frontier. However, in practice it is difficult for some units to achieve that specified target in a single step. Consequently, developing a methodology for assisting units to reach their corresponding targets, through a path of intermediate improving targets, is useful. This problem is investigated in this paper, and we propose a stepwise target setting approach which provides a path of intermediate targets for each unit. We study efficient and inefficient units separately and provide two distinct models for each category, although both of them are intrinsically similar. A simple numerical example and an application are also provided to illustrate our approach.
机译:原始数据包络分析模型将决策单位视为独立实体。数据包络分析的此功能导致输入和输出权重的显着差异,从管理的角度来看这是无关紧要的且有问题。在这方面,已经开发了几种方法来测量基于共同权重的效率得分。具体来说,Ruiz和Sirvant(Omega 65:1–9,2016)制定了汇总的DEA模型,以最大程度地减少实际绩效与最佳实践之间的差距,并确定常见的最佳实践前沿。他们的模型能够同时确定所有被评估单位的目标单位,而且它们都位于共同的最佳实践边界上。但是,实际上,某些单位很难一步实现该指定目标。因此,开发一种通过中间改进目标的途径来协助单位达到其相应目标的方法是有用的。本文将对此问题进行研究,我们提出了一种逐步设置目标的方法,该方法为每个单元提供了中间目标的路径。我们分别研究有效和无效的单元,并为每个类别提供两个不同的模型,尽管它们本质上是相似的。还提供了一个简单的数值示例和一个应用程序来说明我们的方法。

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