数据包络分析中基于相对相似单元的决策单元排序

S. H. Nasseri, O. Gholami, A. Ebrahimnejad
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引用次数: 8

摘要

数据包络分析(DEA)是一种重要的数学规划技术,用于测量具有多输入和多输出的一组决策单元(dmu)的相对效率。传统的DEA帮助决策者在同质群体中区分高效和低效的dmu。然而,它并没有提供更多关于高效dmu的信息。本文提出了一种区分高效机组性能的新方法。该方法通过在可能性生产集中引入与有效单元相关联的虚拟单元——相对相似单元,构建了一种新的DEA模型。我们提出了两个实际的例子来证明所提出的方法的适用性,并展示了程序的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On ranking decision making units using relative similar units in data envelopment analysis
Data envelopment analysis (DEA) is a considerable mathematical programming technique for measuring the relative efficiencies of a set of decision making units (DMUs) with multiple inputs and multiple outputs. Conventional DEA assists decision makers in distinguishing between efficient and inefficient DMUs in a homogeneous group. However, it does not give more information about the efficient DMUs. In this paper, a new method is proposed to differentiae the performance of efficient units. In the proposed method a new DEA model is constructed by introducing some virtual units called relative similar units associated with efficient units into the possibility production set. We present two practical examples to demonstrate the applicability of the proposed method and to exhibit the efficacy of the procedures.
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