Fuzzy Data Envelopment Analysis with Ordinal and Interval Data

In this paper, we reformulate the conventional DEA models as an imprecise problem and propose a novel method for evaluating DMUs when inputs outputs are fuzzy and/or ordinal or vary in intervals. For purpose, convert all data into interval data. order to each number data, use nearest weighted approximation of numbers by applying weighting function, one. manner, could The presented determine efficiencies DMUs. To rank based on their associated efficiencies, first apply Ω-index that is developed ranking numbers. Then, introducing ideal DMU, efficient present complete ranking. Finally, one example illustrate process real application health care show usefulness proposed approach. evaluation, consider interval, ordinal, alongside precise evaluate 38 hospitals selected OIG. results reveal capabilities deal with