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IMPROVEMENTS TO SMOOTH DATA ENVELOPMENT ANALYSIS

机译:平滑数据包络分析的改进

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摘要

Classic data envelopment analysis (DEA) models do not provide unique solutions for multipliers of extreme efficient units. To overcome this problem, previous works have proposed the smooth DEA technique. However, multidimensional models with variable returns to scale (BCC) present deficiencies, as they do not fully ensure the frontier's convexity. Therefore, the main contribution of this paper is to correct smooth BCC models, with regard to such property. Moreover, we propose improvements to smooth DEA models, so that all projections for the evaluated units, and their efficiency values, are non-negative. Furthermore, based on the corrected and improved smooth model, we propose a solution to avoid a classic BCC distortion, which may be called efficiency by default. Finally, we evaluate the operational performance of Brazilian airlines in 2010, to show the applicability of our model and to illustrate the practical effect of our contributions.
机译:经典数据包络分析(DEA)模型无法为极高效单位的乘数提供独特的解决方案。为了克服这个问题,先前的工作提出了平滑DEA技术。但是,具有可变规模收益(BCC)的多维模型存在缺陷,因为它们不能完全确保边界的凸性。因此,本文的主要贡献在于针对此类属性校正平滑的BCC模型。此外,我们提出了对平滑DEA模型的改进,以使所评估单位的所有预测及其效率值均为非负数。此外,基于校正和改进的平滑模型,我们提出了一种避免经典BCC失真(默认情况下称为效率)的解决方案。最后,我们评估了巴西航空公司2010年的运营绩效,以展示我们模型的适用性并说明我们所做贡献的实际效果。

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