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首页> 外文期刊>International journal of information and decision sciences >Slacks-based measurement models for estimating returns to scale
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Slacks-based measurement models for estimating returns to scale

机译:基于松弛的度量模型,用于估算规模收益

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摘要

Slacks-based measurement (SBM), as an important branch of data envelopment analysis (DEA) for evaluating the efficiency of decision making units (DMUs) was firstly proposed by Tone and has been well developed in different directions. Many models based on SBM have been built, such as SUM of super efficiency, SBM of network DEA, SBM of the worst-practise DEA and dynamic SBM. In this paper, we extend the SBM models in different returns to scale (RTS) and build a comprehensive SBM model. Based on the SBM models above, a new approach for estimating returns to scale of DMUs is proposed. This approach has a specific advantage that it can gain projection of inefficient observations from input-output orientation. At last, a numerical example demonstrates the process of estimating returns to scale.
机译:基于松弛的度量(SBM)作为数据包络分析(DEA)评估决策单元(DMU)效率的重要分支,是Tone首次提出的,并已在不同方向上得到了很好的发展。建立了许多基于SBM的模型,例如超效率SUM,网络DEA的SBM,实践最差的DEA的SBM和动态SBM。在本文中,我们将SBM模型扩展到不同的规模收益(RTS),并建立一个全面的SBM模型。基于以上的SBM模型,提出了一种估计DMU规模收益的新方法。这种方法具有一个特殊的优势,即可以从输入输出方向获得对无效观测值的投影。最后,通过一个数值例子说明了规模收益估算的过程。

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