International Journal of Data Envelopment Analysis
Volume:4 Issue: 2, May 2016

Evaluating of staff’s performance and accessing to optimized performance in an organization will be possible by creating factors such as job satisfaction and Organizational Commitment. In this paper, Data Envelope Analysis (DEA) was used in order to evaluate staffs’ performance in Khuzestan Oxin Steel Company. Also, a questionnaire was used in order to collect necessary data and information (Cronbach's Alpha= 0.82). This study used CCR model because of constant yield to scale the most of DMUs. The Inputs research consists of education, salary, relationship with colleagues, job security, workplace, organizational structure, benefits, and rewardswhile outputs are job satisfaction and Organizational Commitment. The results indicate that the amount of work was the main factor between performance of DMUs while higher salary was the most effective factor in staffs’ satisfaction.

One of the new topics in DEA is the data with integer values. In DEA classic models, it is assumed that input and output variables have real values. However, in many cases, some inputs or outputs can have integer values. Measuring cost efficiency is another method to evaluate the performance and assess the capabilities of a single decision-making unit for manufacturing current products at a minimum cost with its input prices. In this paper, we proposed a model which is capable of calculating the cost efficiency in the absence of convexity when some of the input parameters have integer values, and then we implemented the mentioned model with a numerical example and discussed the results.

In this paper, we consider the production possibility set with n production units such that the following four principles that governs: inclusion observations, conceivability, immensity and convexity. Our goal is to estimate the output of a same and new production unit with existing production possibility and amount of input is specified. So, initially we find the interval changes of each inputs and outputs and then partitioning each interval with fuzzy numbers. We use each of the possible to produce with specific inputs and outputs the fuzzy if-then rules and estimate the amount of outputs with mamdany method in fuzzy inference mechanism. In the end, we present an example of implementation.

Data envelopment analysis (DEA) is a nonparametric approach to estimate relative efficiency of Decision Making Units (DMUs). DEA and is one of the best quantitative approach and balanced scorecard (BSC) is one of the best qualitative method to measure efficiency of an organization. Since simultaneous evaluation of network performance of the quad areas of BSC model is considered as a necessity and separate use of DEA and BSC is not effective and leads to miscalculation of performance, integrated DEA-BSC model is applied. Regarding to multi-objective nature of the proposed model, two techniques including goal programming and weighted average method are used to solve such problems. At the end of the study, based on data relating to indexes of quad areas of BSC model, the results of the mentioned methods is compared. Besides assessing validation of the proposed model, the overall efficiency and each of the different stages of BSC is obtained. So that, finding a model for decision making units in various stages of BSC is the innovation of this research study.

Data envelopment analysis provides a theoretical framework for analyzing performance and measuring efficiency. Using data envelopment analysis, we can evaluate units and organizations and discuss about their efficiency or inefficiency. In performance assessment of organizations, the most important goal is to rank the units according to their efficiency. Since ranking decision making units in data envelopment analysis may lead us to several efficient units, choosing the best efficient unit is very important. This study evaluates the research efficiency of Islamic Azad University of Golestan province applying DEA, and a new method «OEP / AHP» and finally offers a full ranking with respect to the exit.

Credit scoring is a kind of binary classification problem that contains important information for manager to make a decision in particularly in banking authorities. Obtained scores provide a practical credit decision for a loan officer to classify clients to reject or accept for payment loan. For this sake, in this paper a data envelopment analysis- discriminant analysis (DEA-DA) approach is used for reclassifying  client  to reject or accept class for case of real data sets of  an Iranian bank branch.  For this reason, two DEA models are solved. Also, the reject and accept frontiers and overlapping region among two frontiers are obtained.  Then a goal programming problem is solved for finding co-efficients of the discriminant hyper-plane. The results are obtained from the samples are kept from the main dataset, clarify that the classified hyper-plane obtained from the used method provides an almost profitable classification for payment loan.