International Journal of Data Envelopment Analysis
Volume:11 Issue: 3, Summer 2023

Evaluating the efficiency of low-fee loans in small businesses and comparing them with each other can be a support for strategic planning. Banks and escape from economic inflation. One of the methods of measuring efficiency is data coverage analysis. In this research, the indicators affecting the performance of low-fee loans have been researched and identified, and then the researched samples have been estimated. In three parts, the evaluation of the public institution of Resalat (Qarz Al-Hasna Resalat Bank) which is active in paying microloans has been done and in order to measure the impact of microloans on business creation, it has been compared with 9 other banks. In fact, the purpose of this research is to measure the efficiency of micro-bank facilities and its impact on the creation of micro- and home-based businesses and to identify indicators that affect micro-businesses. At the end, the steps of the research are described with a practical example

One of the interesting subjects that amuse the mind of researchers is surmising the correct classification of a new sample by using available data. Data Envelopment Analysis (DEA) and Discriminant Analysis (DA) can classify data by each one alone. DEA classifies as efficient and inefficient groups and DA classify by historical data. Merge these two methods is a powerful tool for classifying the data. Since, in the real world, in many cases we do not have the exact data, so we use imprecise data (e.g. fuzzy and interval data) in these cases. So, in this paper, we represent our new DEA-DA method by using Mixed-Integer Nonlinear Programming (MINLP) to classify with imprecise data to more than two groups. Then we represent an empirical example of our purpose method on the Iranian pharmaceutical stock companies' data. In our research, we divided pharmaceutical stock companies into four groups with imprecise data (fuzzy and interval data). Since, most of the classical DA models used for two groups, the advantage of the proposed model is beheld. The result shows that the model can predict and classify more than two groups (as many as we want) with imprecise data so correct.

Cross-efficiency is a frequently used method for ranking decision-making units in Data Envelopment Analysis (DEA). A fundamental weakness of this method which has been quite problematic is the presence of multiple optimal weights along with selection of zero values by many of these multiple weights in calculating cross-efficiency. In the current paper it is tried to provide a method which through utilizing fair distribution of weights resolve the mentioned problems and in this way give more validity to the cross-efficiency method in raking decision-making units.

Data Envelopment Analysis (DEA) is a non-parametric mathematical programming method used to assess performance and measure the efficiency of Decision-making Units (DMUs) that operate with multiple concurrent inputs and outputs. The performance of these units is influenced by the utilization of input resources. While an increase in input utilization typically leads to higher production levels, there are scenarios where increased input usage results in decreased outputs. This phenomenon is termed congestion. Given that alleviating congestion can reduce costs and enhance production, it holds significant importance in economics. This paper introduces a method for identifying congestion based on a defined modeling framework. A DMU is considered congested when reducing inputs in at least one component leads to increased outputs in at least one component, and increasing inputs in at least one component can be achieved by reducing outputs in at least one component, without improvement in other indicators. The paper explores congestion in DMUs with both increasing and decreasing inputs.