Neurotic Fuzzy-Data-Envelopment Analysis to Forecast Efficiency of Bank Branches

In this study the prediction of efficiency of four different Bank Branches have been done by using Neurotic Fuzzy Data Envelopment Analysis approach. In the first stage of the study, Artificial Neural Network (ANN) model has been modelled and trained using the last five years data. The data belonging any year has been taken as input of ANN, next year data has been defined as output of ANN. Fuzzyfication process has been applied to obtained predictions based on asking managers of bank branches, after Fuzzy Data Envelopment Analysis process has been applied to fuzzy values. As a result, the bank branches parameters belonging to 2021 year have been obtained. The efficiency of 2021 for bank branches have been calculated based on Fuzzy Data Envelopment Analysis (FDEA).

Evaluation of the Efficiency of European Health Systems Using Fuzzy Data Envelopment Analysis

Many studies that assess efficiency in health systems are based on output mean values. That approach ignores the representativeness of the average statistic, which can become a serious problem in estimation. To solve this question, this research contributes in three different ways: the first aim is to evaluate the technical efficiency in the management of European health systems considering a set of DEA (Data Envelopment Analysis) and FDEA (Fuzzy Data Envelopment Analysis) models. A second goal is to assess the bias in the estimation of efficiency when applying the conventional DEA. The third objective is the evaluation of the statistical relationship between the bias in the efficiency estimation and the macroeconomic variables (income inequality and economic freedom). The main results show positive correlations between DEA and FDEA scores. Notwithstanding traditional DEA models overestimate efficiency scores. Furthermore, the size of the bias is positively related to income inequality and negative with economic freedom in the countries evaluated.

Fuzzy data envelopment analysis in the presence of undesirable outputs with ideal points

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.