基于数据包络分析的规模效益与效率度量新模型

Daneshian Behrouz, Monzeli Abbas Abbasali, G. Tohidi, Razavyan Shabnam, Sanei Masud
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引用次数: 0

摘要

我们将数据包络分析(DEA)中的收益尺度概念推广到权重限制环境。通过添加权重限制,可能需要改变按比例回报的状态,即增加、不变和减少。我们首先定义了“按规模回报”的减重限制,并提出了一种识别按规模回报状态的方法。然后,我们证明了这种增加通常会缩小最生产规模(MPSS)的区域。最后,对于一个低效决策单元(DMU),我们将给出一个简单的规则来确定其预测DMU规模的回报状态。在这里,我们进行了一项实证研究,将所提出的方法的结果与BCC模型进行比较。此外,我们还演示了两种模型中MPSS的变化。我们提出了不同的DEA模型来确定规模收益。在这里,我们提出了一个模型来确定决策单位的整体状态。提出了确定规模收益的不同DEA模型。在这里,我们提出了一个模型来确定决策单位的整体状态。
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A new model to Measuring efficiency and returns to scale on Data Envelopment Analysis
We extend the concept of returns to scale in Data Envelopment Analysis (DEA) to the weight restriction environments. By adding weight restrictions, the status of returns to scale, i.e. increasing, constant, and decreasing, may need a change. We first define "returns to scale" underweight restrictions and propose a method for identifying the status of returns to scale. Then, we demonstrated that this addition would usually narrow the region of the most productive scale size (MPSS). Finally, for an inefficient decision-making unit (DMU), we will present a simple rule for determining the status of returns to the scale of its projected DMU. Here, we carry out an empirical study to compare the proposed method's results with the BCC model. In addition, we demonstrate the change in the MPSS for both models. We have presented different models of DEA to determine returns to scale. Here, we suggested a model that determines the whole status to scale in decision-making units.Different models of DEA to determine returns to scale are presented. Here, we suggested a model that determines the whole status to scale in decision-making units.
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