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Extension of Ranking Method Based On Effectiveness Of units In Society by Common Weights Approach in stochastic DEA

机译:基于社会单位效能的排名方法延伸在随机DEA中的共同重量方法

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The aim of this paper is to modify the suggested method by Noura et al. (A.A. Noura, F. Hosseinzadeh Lotfi, G.R. Jahanshahloo, S. Fanati Rashidi, Super-efficiency in DEA by effectiveness of each unit in society, Applied Mathematics Letters, 24, 2010, 623-626), that is a ranking method based on the effectiveness of each unit in society. They utilized the assigned weights for ranking DMUs, but this is not a conventional method for determining the weights. This paper proposes common weights approach for improving its method. A multi-objective linear fractional is derived and then it was converted to a multi-objective linear programming by Taylor series. The model is solved by Max Min method. Based on the obtained optimal solution, common weights are acquired and then DMUs will be ranked. And we introduce stochastic version of this model in DEA. The deterministic equivalent of this stochastic model will be obtained. The proposed method is illustrated by ranking Taiwan forests after reorganization.
机译:本文的目的是通过Noura等人修改建议的方法。 (AA NORA,F. Hosseinzadeh Lotfi,GR Jahanshahloo,S.Fanati Rashidi,通过每个单位的效率在社会中的有效性,应用数学字母,24,100,623-626),这是一种基于的排名方法每个单位在社会中的有效性。它们利用用于排名DMU的分配权重,但这不是确定权重的传统方法。本文提出了改进其方法的共同重量方法。衍生多目标线性分数,然后将其转换为泰勒系列的多目标线性编程。 MAX MIN方法解决了该模型。基于所获得的最佳解决方案,获取普通权重,然后DMU将排列。我们在DEA中引入了这个模型的随机版本。将获得该随机模型的确定性等同物。所提出的方法是通过重组后排名的台湾森林来说明。

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