International Journal of Data Envelopment Analysis
Volume:5 Issue: 3, Jul 2017

Data envelopment analysis (DEA) is a common technique in measuring the relative efficiency of a set of decision making units (DMUs) with multiple inputs and multiple outputs. ‎‎Standard DEA models are ‎‎quite limited models‎, ‎in the sense that they do not consider a DMU ‎‎at different times‎. ‎To resolve this problem‎, ‎DEA models with dynamic ‎‎structures have been proposed‎.‎In a recent paper by afarian-Moghaddam and Ghoseiri [Jafarian-Moghaddam, A.R., Ghoseiri k., 2011. Fuzzy dynamic multi-objective Data Envelopment Analysis model. Expert Systems with Applications, 38 (1), 850-855.] they contribute to an interesting topic by presenting a ‎‎fuzzy dynamic multi-objective DEA model to evaluate DMUs in which ‎‎data are changing with time‎. However, this paper finds that their approach has some problems in the proposed models. In this paper, we first stress the present shortcomings in their modeling and then we propose a new DEA method for improving fuzzy dynamic multi-objective DEA model. The proposed model is a ‎‎multi-objective non-linear programming (MONLP) problem and there are ‎‎several methods for solving it; We use the goal programming (GP) method‎. ‎The proposed model calculates the efficiency scores of DMUs by‎‎ solving only one linear programming problem‎. ‎Finally‎, ‎we present an ‎‎example with ten DMUs at three times to illustrate the applicability the proposed model.

Data envelopment analysis (DEA) creates many opportunities for collaboration between analyst and decision-maker. There are, however, situations in which all of the decision-making units (DMUs) fall under the umbrella of a centralized decision maker that oversees them. Many organizations such as bank branches, chain stores, … can do this. This centralized decision maker unit expect that resource allocation and revenue efficiency be in a way that DMUs not separately but in a group and simultaneously projected onto the efficiency frontier; as a result, it won’t be possible based on current DEA models. Therefore, centralized resource allocation or institutional allocation was formulated. There are situations in which centralized method presented in a central decision maker unit to allocate resources based on revenue efficiency. However, in reality value and rate are not often observed for all of the undesirable and desirable output units, which poses a problem in determining the revenue efficiency. Therefore, the best solution in these cases is to divide the outputs into two categories of known and unknown prices, which will be a more valid criterion for determining the revenue efficiency. In this paper, based on these methods, the revenue efficiency in branches of Pasargadae Bank will be analyzed and a comprehensive ranking will be made on these branches.

We define a combined DEA score to evaluate efficiency in agricultural research. The production model we propose considers efficiency measurements under variable returns to scale for each year in the period 2012–2017. We postulate a first-order autoregressive process in the presence of covariates, to explain efficiency. Powers of the autocorrelation coefficient estimated assuming a dynamic panel specification, are used as weights to determine a combined efficiency score. A higher weight is given to recent efficiency measurements. We use a fractional regression model to investigate the statistical significance of covariates on the combined score further.

Congestion indicates an economic state where inputs are overly invested. Evidence of congestion occurs whenever reducing some inputs can increase outputs. In this paper, we present a new model to identify and evaluate congestion in Data Envelopment Analysis (DEA). We use output efficient DMUs to construct our proposed model to evaluate congestion. We also proposed a linear inequality and equality system to identify the occurrence of congestion. Finally, three numerical examples are presented to illustrate the use of our proposed method.

Financial ratios provide an illustration of the financial situation, the company's returns, and the future opportunities of business units. Considering that in traditional methods, the effect of financial ratios on efficiency has been investigated so far, it did not look right; therefore, we sought a method that It can be used to see the effect of financial ratios. In the process of data envelopment analysis, financial ratios are aggregated and we can see the effect of them together; therefore, the efficiency gained from this approach is reliable. In this research, the data envelopment analysis model was used as input-based BCC type, and the relative efficiency of 470 companies accepted in the Tehran Stock Exchange between 1392-1395 Which is classified into nine non-homogeneous groups, was calculated, then the companies were ranked based on efficiency; efficient and inefficient companies were identified,It was determined that based on the average industry obtained for the future financial status of the companies, analysis and forecasting took place, among which the construction materials group with the average industry was 0.89 at the highest and the investment group with the average industry was 0.67 at the lowest.

Paying attention to the health of people in the community is one of the main challenges that has received much attention in recent years. Regarding the importance of the healthcare sector in the life of the community, evaluation of the performance of this sector is important. One of the most practical methods for evaluating performance is the use of data envelopment analysis approach. In this paper, changes 's performance of healthcare sector of Iranian provinces in terms of their performance in 1393-1396, using data envelopment analysis along with window analysis are considered. The results show that during 1393-1396, the performane of healthcare sector of the provinces West Azarbaijan, Zanjan, Qazvin, Kohkiluyeh and Boyer Ahmad have improved. performance of healthcare sector of the provinces Isfahan, North Khorasan, Kerman, and Yazd have gotten worse.