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An Improvement of DEA Cross-Efficiency Aggregation Based on BWM-TOPSIS

机译:基于BWM-TOPSIS的DEA交叉效率聚集的改进

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Data Envelopment Analysis (DEA) cross-efficiency has been used to replace the self-evaluation system, which requires decision-makers to rank a series of decision-making units (DMUs) according to cross-efficiency scores, and finally determine the order of each DMU, so as to provide decision-making basis for decision-makers. However, this method has certain deficiencies: one is that the cross-efficiency value is not unique; the other one is that the cross-efficiency evaluation uses the same weight to aggregate cross-efficiency. In this paper, Technique for order performance by similarity to ideal solution (TOPSIS) and The Best Worst Method (BWM) methods are used to aggregate the aggressive cross-efficiency values and benevolent cross-efficiency values. Besides, the high consistency of the BWM method improves the shortcomings of the TOPSIS method. The result of TOPSIS method is not necessarily close to the ideal solution, and far away from the negative ideal solution. At the same time, the distance measured by the TOPSIS method replaces the preference of the BWM method, which makes BWM method more objective. Finally, a numerical example is given to verify the feasibility and effectiveness of the method.
机译:数据包络分析(DEA)的交叉效率已被用于取代自我评估系统,这需要决策者根据交叉效率分数排列一系列决策单位(DMUS),并且最终确定的顺序每个DMU,以便为决策者提供决策。但是,这种方法具有一定的缺陷:一个是交叉效率值并不唯一;另一个是交叉效率评估使用相同的重量来聚合交叉效率。在本文中,使用与理想解决方案(TOPSIS)的相似性的顺序性能和最佳最坏的方法(BWM)方法来聚合攻击性交叉效率值和仁慈的交叉效率值。此外,BWM方法的高一致性提高了TOPSIS方法的缺点。 Topsis方法的结果不一定接近理想的解决方案,远离负面理想解决方案。同时,通过TopSIS方法测量的距离取代了BWM方法的偏好,这使得BWM方法更客观。最后,给出了一个数值例子来验证方法的可行性和有效性。

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