Data envelopment analysis (DEA) is a non-parametric methodology for efficiency assessment. This paper proposes a new radial, input-oriented and fully fuzzy DEA approach, based on an LU-fuzzy partial order (L for lower, U for upper), for assessing the relative efficiency of a set of Decision-Making Units (DMUs). The proposed approach involves a radial input contraction, Phase I, and an additive slacks maximization, Phase II. Each phase is first formulated as a fully fuzzy linear programming (FFLP), and then it is transformed into a multiobjective optimization problem. The latter is solved using the lexicographic weighted Tchebycheff method. The proposed fully fuzzy DEA approach provides, for each unit, a fuzzy efficiency measure and a fuzzy target operating point. A classification of the efficiency status of the units is also presented. Computational experiences and comparison with other fuzzy DEA approaches are reported.

中文翻译：

---

使用完全模糊DEA方法的效率评估和目标设定

数据包络分析（DEA）是效率评估的非参数方法。本文提出了一种新的径向，面向输入的，完全模糊的DEA方法，该方法基于LU模糊偏序（L代表较低，U代表较高），用于评估一组决策单元（DMU）的相对效率。 。所提出的方法涉及径向输入收缩（阶段I）和累加松弛最大化（阶段II）。每个阶段首先被公式化为完全模糊线性规划（FFLP），然后将其转化为多目标优化问题。后者使用词典编纂的加权Tchebycheff方法求解。所提出的完全模糊DEA方法为每个单元提供了一个模糊效率测度和一个模糊目标工作点。还提供了单位效率状态的分类。