Data envelopment analysis (DEA) is a prominent technique for evaluating relative efficiency of a set of entities called decision making units (DMUs) with homogeneous structures. In order to implement a comprehensive assessment, undesirable factors should be included in the efficiency analysis. The present study endeavors to propose a novel approach for solving DEA model in the presence of undesirable outputs in which all input/output data are represented by triangular fuzzy numbers. To this end, two virtual fuzzy DMUs called fuzzy ideal DMU (FIDMU) and fuzzy anti-ideal DMU (FADMU) are introduced into proposed fuzzy DEA framework. Then, a lexicographic approach is used to find the best and the worst fuzzy efficiencies of FIDMU and FADMU, respectively. Moreover, the resulting fuzzy efficiencies are used to measure the best and worst fuzzy relative efficiencies of DMUs to construct a fuzzy relative closeness index. To address the overall assessment, a new approach is proposed for ranking fuzzy relative closeness indexes based on which the DMUs are ranked. The developed framework greatly reduces the complexity of computation compared with commonly used existing methods in the literature. To validate the proposed methodology and proposed ranking method, a numerical example is illustrated and compared the results with an existing approach.

数据包络分析（DEA）是一种用于评估一组实体的相对效率的杰出技术，这些实体被称为具有相同结构的决策单元（DMU）。为了进行全面评估，效率分析中应包括不良因素。本研究致力于提出一种新颖的方法，用于在不期望的输出存在下求解DEA模型，其中所有输入/输出数据均由三角模糊数表示。为此，在拟议的模糊DEA框架中引入了两个虚拟模糊DMU，分别称为模糊理想DMU（FIDMU）和模糊反理想DMU（FADMU）。然后，使用词典方法来分别找到FIDMU和FADMU的最佳模糊效率。此外，所得的模糊效率用于测量DMU的最佳和最差模糊相对效率，以构建模糊相对接近度指标。为了解决总体评估问题，提出了一种新的方法来对模糊相对亲和度指标进行排名，并以此为基础对DMU进行排名。与文献中常用的现有方法相比，开发的框架大大降低了计算的复杂性。为了验证提出的方法和提议的排序方法，给出了一个数值示例，并将结果与​​现有方法进行了比较。与文献中常用的现有方法相比，开发的框架大大降低了计算的复杂性。为了验证提出的方法和提议的排序方法，给出了一个数值示例，并将结果与​​现有方法进行了比较。与文献中常用的现有方法相比，开发的框架大大降低了计算的复杂性。为了验证提出的方法和提议的排序方法，给出了一个数值示例，并将结果与​​现有方法进行了比较。