Data covering analysis is a technique for evaluating the performance of homogeneous decision-making units. In evaluating the performance of units, it is necessary the efficiency and ranking of units be done (be calculated). Ranking decision-making units is based on efficiency. According to the existence of various efficacies in the applied examples, different techniques have been developed for ranking, that in this study, cross efficiency method has been used. This technique has been developed for fuzzy input and output modes. While data are fuzzy, cross efficiency table has been calculated inaccurately and table entries are fuzzy numbers, so the average (mean) of calculated efficiency in the table is also a fuzzy number. Since comparing the averages of obtained efficiencies ranks the decision-making units, it is necessary to propose a solution for this comparison. In this research, after providing the required grounds with assistance of models and doing the necessary changes in them, the averages of efficiency are compared by assistance of fuzzy ranking function. And with the knowledge that every decision-making unit that has better average (mean) is more efficient, decision-making units are ranked. پرونده مقاله

When we use the CCR model in the input-oriented with fuzzy data for ranking with the help of cross-efficiency, there is a possibility that the model will find a different optimal answer. This means that the ranking is not unique, that is, a decision-making unit may be assigned several ranks. Here, the judgment regarding the ranking faces a problem. To solve this problem, a secondary objective is determined for weight selection. According to that secondary objective, a suitable weight is selected from among the optimal solutions. In this article, the secondary goal of the concept of symmetrizing the weights plays a fundamental role in solving the mentioned problem. The model selects weights that are symmetrical, the act of choosing symmetrical weights causes many weights that are not useful to be removed from the set. The decision-making unit that selects symmetrical weights for all indicators, has a better performance than the decision-making unit that does not use symmetrical weights and covers its weak points with low weight and highlights its strong points with high weight. The model along with the mentioned secondary objective is used to evaluate decision-making units with fuzzy input and output, by choosing the optimal weight, a cross-efficiency table is formed. By using the cross-efficiency table, the efficiency of each unit is determined and ranked compared to other units. Units are done. پرونده مقاله