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Entropy Cross-Efficiency Model for Decision Making Units with Interval Data

机译:区间数据的决策单元熵交叉效率模型

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The cross-efficiency method, as a Data Envelopment Analysis (DEA) extension, calculates the cross efficiency of each decision making unit (DMU) using the weights of all decision making units (DMUs). The major advantage of the cross-efficiency method is that it can provide a complete ranking for all DMUs. In addition, the cross-efficiency method could eliminate unrealistic weight results. However, the existing cross-efficiency methods only evaluate the relative efficiencies of a set of DMUs with exact values of inputs and outputs. If the input or output data of DMUs are imprecise, such as the interval data, the existing methods fail to assess the efficiencies of these DMUs. To address this issue, we propose the introduction of Shannon entropy into the cross-efficiency method. In the proposed model, intervals of all cross-efficiency values are firstly obtained by the interval cross-efficiency method. Then, a distance entropy model is proposed to obtain the weights of interval efficiency. Finally, all alternatives are ranked by their relative Euclidean distance from the positive solution.
机译:交叉效率方法是数据包络分析(DEA)的扩展,它使用所有决策单元(DMU)的权重来计算每个决策单元(DMU)的交叉效率。交叉效率方法的主要优点是它可以为所有DMU提供完整的排名。此外,交叉效率方法可以消除不切实际的重量结果。但是,现有的交叉效率方法仅使用输入和输出的精确值评估一组DMU的相对效率。如果DMU的输入或输出数据(例如间隔数据)不准确,则现有方法无法评估这些DMU的效率。为了解决这个问题,我们建议将香农熵引入交叉效率方法。在提出的模型中,首先通过区间交叉效率法获得所有交叉效率值的区间。然后,提出了一种距离熵模型来获得区间效率的权重。最后,所有替代方案均按其与正解的相对欧几里得距离进行排名。

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