Basic Data Envelopment Analysis (DEA) models are designed for non-negative data. However, negative data is inevitably used in many real-world issues. Also, multiple units with a maximum relative performance score (equal to one) can be obtained due to the benevolent view of evaluating Decision Making Units (DMUs) consistent performance. Therefore, the researchers proposed ranking models to differentiate efficient units. Cross efficiency is one of the most useful tools for DMUs ranking in the DEA. There are two major drawbacks to implementing this process. First, it gives different results in the presence of other optimal solutions; second, it does not provide a compelling reason to use the arithmetic mean to aggregate the results of the cross efficiency matrix. In this paper, first a new non-radial model is proposed to evaluate the performance of DMUs in the presence of negative data and then based on this model a new secondary goal model is proposed to eliminate the first drawback in the cross efficiency method. Also, to solve the second drawback in this method, a hybrid Multi-Attribute Decision Making (MADM)-DEA process with the help of fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje method is proposed. Finally, to show the applicability of the proposed methods, the results are used to select the supplier in a real-world problem.

基本数据包络分析 (DEA) 模型专为非负数据而设计。然而，在许多现实世界的问题中不可避免地会使用负面数据。此外，由于评估决策单元 (DMU) 一致性能的仁慈观点，可以获得具有最大相对性能分数（等于 1）的多个单元。因此，研究人员提出了排序模型来区分有效单元。交叉效率是 DMU 在 DEA 中排名的最有用的工具之一。实施此过程有两个主要缺点。首先，它在存在其他最优解的情况下给出了不同的结果；其次，它没有提供使用算术平均值来聚合交叉效率矩阵结果的令人信服的理由。在本文中，首先提出了一个新的非径向模型来评估存在负数据的 DMU 的性能，然后基于该模型提出了一个新的次要目标模型来消除交叉效率方法中的第一个缺点。此外，为了解决该方法的第二个缺点，提出了一种借助模糊 VlseKriterijumska Optimizacija I Kompromisno Resenje 方法的混合多属性决策 (MADM)-DEA 过程。最后，为了显示所提出方法的适用性，结果用于在实际问题中选择供应商。提出了一种借助模糊 VlseKriterijumska Optimizacija I Kompromisno Resenje 方法的混合多属性决策 (MADM)-DEA 过程。最后，为了显示所提出方法的适用性，结果用于在实际问题中选择供应商。提出了一种借助模糊 VlseKriterijumska Optimizacija I Kompromisno Resenje 方法的混合多属性决策 (MADM)-DEA 过程。最后，为了显示所提出方法的适用性，结果用于在实际问题中选择供应商。