International Journal of Data Envelopment Analysis
Volume:7 Issue: 1, Jan 2019

Data envelopment analysis (DEA) is a powerful technique for performance evaluation of decision making units (DMUs). One of the main objectives that is followed in performance evaluation is discriminating among efficient DMUs to provide a complete ranking of DMUs. DEA successfully divides them into two categories: efficient DMUs and inefficient DMUs. The DMUs in the efficient category have identical efficiency score. But the question that raises here is in evaluation. Where several DMUs have the equal efficiency, which unit performs better and how can we rank these efficient units, Different methods have been presented for ranking the efficient units. In this paper, we propose a method for calculating an efficiency of DMUs by comparing with the bad benchmark line. Our approach obtain common set of weights to create the best efficiency score, such that the amount of DMUs that are efficient is less than that of other models. If we have more than one efficient DMU, we can rank them by the same model and it isn't necessary to use another ranking method.

Cooper et al. [Efficiency aggregation with enhanced Russell measures in data envelopment analysis, Socio-Economic Planning Sciences, 41 (2007) 1–21] presented a method for measuring aggregate efficiency, using the enhanced Russell measure. In that paper, they posed questions and opened the way for new opportunities for studying and extending the proposed method and some other related fields. One of these issues is the extension of the proposed method in the case where there is the possibility of reallocation among the units in order to improve group efficiency. In this paper, we propose a model for evaluating the group efficiency, and employ the centralized scenario to set targets for each unit as well as for the group.

Data Envelopment Analysis (DEA) is a mathematic technique to evaluate the relative efficiency of a group of homogeneous decision making units (DMUs) with multiple inputs and outputs. The efficiency of each unit is measured based on its distance to the production possibility set (PPS). In this paper, the BCC model is used in output-oriented. The average return on profit as output and the covariance of profit (risk) are considered as inputs. In the continuation, the median and the mod earned investment as two factors of output to the model presented to provide a better analysis of the types of investment, and finally, let us mention a true example

Financial returns exhibit stylized facts such as leptokurtosis, skewness and heavy-tailness. Regarding this behavior, in this paper, we apply multivariate generalized hyperbolic (mGH) distribution for portfolio modeling and performance evaluation, using conditional value at risk (CVaR) as a risk measure and allocating best weights for portfolio selection. Moreover, a robust portfolio optimization and performance evaluation modeling in mGH framework are developed, using worst case CVaR (WCVaR) as a risk measure. Due to the fact that expected returns can take negative values, the introduced model is inspired by Range Directional Measure model. Finally, real data in Iran stock market are given to illustrate the effectiveness of the model.

In the Iranian economy, banks play a key role in financing and developing the capital market.Therefore, it is important to evaluate the performance of stock banks. Data Envelopment Analysis (DEA) is a wide range of mathematical models used to measure the relative efficiency for a set of homogeneous decision-making units with similar inputs and outputs. In this paper, a novel efficiency ranking approach is proposed with two flexible mixed models derived from Goal Programming Data Envelopment Analysis (GPDEA) models.To solve this mismatch, we use Gantt chart to show DMUs’ floating ranking and use another model to appoint the ranks exactly. In this paper, we analyze 18 stocks efficiency from bank industry of Tehran Exchange in 2016 using the GPDEA approach. Results demonstrate that the novel efficiency ranking approach has higher ability than the basic models in efficiency ranking

The efficiency of graduate programs is directly linked to a country's capacity for innovation, which entails the need to diagnose the causes of low academic performance, as well as the development of techniques and methods to evaluate and measure the performance of educational units. Thus, the aim of this research is to analyze the efficiency of Brazilian graduate programs using Multiple Regression and Data Envelopment Analysis tools from 2014. In order to do this, a specific area was selected, called Engineering III, which includes production, mechanical, industrial and aerospace engineering programs. The results of this research can contribute to a better understanding of the dynamics and determining factors of the national academic production in order to generate knowledge concerning graduate programs, especially courses that did not meet the technical production efficiency standards required by the Coordination for the Improvement of Higher Education Personnel (CAPES), an organ responsible for graduate studies in Brazil. By analysis, there is a need to reposition the CAPES evaluation process in terms of the variables to be considered, as well as the criteria applied for this.