Introduction: To assess efficiency as a relative concept, the difference between real and expected values must be determined by comparing the productivity of economic units of industry and the efficiency in potential production conditions. As economic units, hospitals need to utilize efficiency ANALYSIS in order to make optimal use of their available resources. Therefore, the present study compared the efficiency of different hospitals of Shahid Beheshti University of Medical Sciences.Methods: Since this applied analytical research referred to the documents and performance statistics of hospitals and therfore used real DATA, evaluating the validity and reliability had no relevance. In order to measure the relative efficiency of health care in 16 hospitals associated with Shahid Beheshti University of Medical Sciences and also to determine efficient and inefficient hospitals during 2005-2007, CCR (Charnes-Cooper-Rhodes) input oriented multiplier model of DATA ENVELOPMENT ANALYSIS (DEA) was used for DATA ANALYSIS. In addition, AP (Anderson-Peterson) model was used to rank efficient hospitals. LINDO software was utilized to solve the model.Results: Based on our results, 43.75%, 31.25% and 37.5% of hospitals were efficient respectively in 2005, 2006 and 2007.Conclusion: The results suggested that the efficiency of inefficient hospitals could be improved through the potential savings in resources (62.44% in infrastructure, 43.88% in the number of active beds, 37.15% in the number of physicians and 38.58% in the number of paramedics).

In this paper, DATA ENVELOPMENT ANALYSIS (DEA) was discussed for non-homogenous mixed NETWORK structure where the outputs of each stage can enter the succeeding stage in the same or another layer as inputs or come out from the system as final products. In this model, in addition to intermediate inputs, each stage can also have independent inputs. To this end, a mathematical model has been developed in which the independent inputs and final outputs for the components of the decision making units (DMUs) are examined. To show the efficiency of the model, real DATA for 20 DMUs were used and the results were compared with those of traditional models. The results obtained from the proposed method eliminated the shortcomings of traditional methods for proper detection of units that are on the efficient border (with an efficiency of one). The developed method can provide managers and decision makers with a more accurate understanding of the performance of the components of DMUs.

NETWORK DATA ENVELOPMENT ANALYSIS (NDEA) is one of the most important branches of DATA envel-opment ANALYSIS (DEA) that is employed in order to performance measurement of decision making units (DMUs) with internal or NETWORK structures. In this study, the window NETWORK DATA envelop-ment ANALYSIS (WNDEA) model will be proposed, that is capable to be used in the presence of panel DATA. Additionally, the proposed model is applied to evaluate the dynamic e ciency of 5 investment companies in Tehran stock exchange during the period from 2013 to 2017. Experimental results show that the proposed window NETWORK DEA model is e ective and employing this model increases the reliability of the results.

Traditional DATA ENVELOPMENT ANALYSIS (DEA) models deal with measurement of relative efficiency of decision making units (DMUs) in which multiple-inputs are consumed to produce multiple-outputs. One of the drawbacks of these models is neglecting internal processes of each system, which may have intermediate products and/or independent inputs and/or outputs. In this paper, some methods which are usable for NETWORK systems are briefly reviewed. A new unified model is also introduced which can be easily applied for performance measurement of all types of NETWORK production process. As an example, for the application of NETWORK DEA models, performance evaluation of wheat production in Iran provinces is considered and the results are compared.

Considering the role of banks in Iran and the significance of appraising their functions, this study evaluates the banks' efficiency during 2010-2014. There are many researches in field of bank's efficiency and productivity with DEA but all of them do not consider their internal structure in surveying of efficiency. Main aim of this paper is to survey efficiency of 18 banks with NETWORK DATA ENVELOPMENT ANALYSIS (NDEA). In this paper, worker, fixed asset and official and public costs are interred to model as input, bank deposit is intermediate variable ( output in first step and input in second step), banks profit is desirable output and Non-performing loans of banks is undesirable output which are considered in model. Variables are interred to model according to their roles in bank's structures. Results reveal that else of 3 banks, all banks of our sample operated less than efficient level in our period. Performance of some banks is so weak and some banks which have more Non Performing loans have lower efficiency.

Providing skills training is an essential need of different societies. Considering the significance of the role of skill training in empowerment and employment of individuals through the training of skilled labor required by the labor market and industry, in this study the supply chain of skills training has been designed. In the proposed supply chain, according to the skill training aspects, a NETWORK structure is conceptualized to include appropriate factors in different layers of the supply chain. Evaluating the performance of the supply chain is handled applying a NETWORK DATA ENVELOPMENT ANALYSIS. NETWORK DATA ENVELOPMENT ANALYSIS (NDEA) is an efficient method analyzing all the factors included in the evaluation NETWORK. Among the NDEA models, the output-oriented BCC model was selected due to the importance of the output of the supply chains of the skills training. In addition to efficiency, the concept of complementary loss is also introduced to validate the results. The research findings show the efficiency of various factors in the stages of the designed NETWORK. On the other hand, unlike the classic DEA method, which shows the maximum efficiency of a factor, in the proposed NETWORK model, the efficiency priority is calculated and the efficiency is determined at each stage of the supply chain.

NETWORK DATA ENVELOPMENT ANALYSIS models assess efficiency of decision-making unit and its sections using historical DATA but fail to measure efficiency of its units and their internal stages in the future. In this paper we aim to measure efficiency of stages of bank branches and obtain efficiency trend of stages during the time, then to estimate their efficiency in the future therefore we can be aware of stages inefficiency before occurrence and prevent them. First, a two-stage structure including deposit collection and loan giving was designed for bank branches using literature review and comments of experts. Human forces and fixed assets were considered as input variables of the first stage, deposit as mediator variable, delayed claims as interim variable, and loan amount as output variable of the second stage. Then, a dynamic NETWORK DATA ENVELOPMENT ANALYSIS model was formulated and stages efficiency were obtained for 16 consecutive periods. Therefore, efficiency trend of stages was obtained during the time. In the following, efficiency of various stages of branches were estimated using artificial neural NETWORK and some recommendations are provided according to obtained amounts in order to prevent inefficiency before occurrence.

Congestion is one of the important concepts in DATA ENVELOPMENT ANALYSIS that occurs when excessive inputs reduce the maximally possible outputs. Identification and elimination of congestion have a significant impact on reducing inputs along with increasing outputs. Hence, various studies have been conducted to detect and evaluate congestion. However, in today's world, no organization can achieve its final output with just one process of input. In other words, today's organizations have a NETWORK structure that consists of several subsections. Ignoring the existing influences among the subsections processes may lead to inadequate or even incorrect results for evaluating the congestion. While all of the existing methods only evaluate the congestion of each subsection or the whole system independently. Therefore, in this paper, the concept of congestion is developed for a specific and so practical case of NETWORK structure called “two-stage NETWORK structure”. This case of NETWORK structure consists of two series stage such that stage 1 consume some primary inputs to produce some intermediate outputs. In the following, the intermediate outputs are used as the inputs of stage 2 to produce the final output. Here, the concept of congestion is defined for systems with a two-stage structure. Then, to examine the congestion of each stage as well as the congestion of the whole system, a single linear programming model is proposed. The validity of the proposed model is investigated using several theorems and it is shown that the new definition is a generalization of the previous definitions of the congestion for the black-box systems. Finally, the proposed model is applied to a case study including 24 non-life insurance companies in Taiwan.

This study investigates the efficiency of total and independent sub-units of a decision-making unit under uncertainty using fuzzy logic and a new method of DATA ENVELOPMENT ANALYSIS. Initially, the shortcomings of standard models were examined, followed by an ANALYSIS of different approaches to NETWORK DATA ENVELOPMENT ANALYSIS. Intermediate measures that should be maximized in the first stage and minimized in the second stage were modelled. Given the ambiguity and uncertainty in the real world, fuzzy logic and the concept of alpha-cut were employed. The proposed fuzzy model, termed the fuzzy min-max method, was validated with existing DATA, and the performance of managers from 14 Iranian airlines was evaluated. The results indicated that Taban, is the only airline efficient in all three stages. Meraj airline, with a total efficiency of 0.35, has the lowest efficiency, primarily due to the first stage. This research demonstrates that evaluating the performance of airline managers using fuzzy NETWORK models can effectively measure managerial efficiency in multi-stage processes and high uncertainty scenarios.

Many products are produced in NETWORKs of firms connected to each other by transactions. From a supply NETWORK perspective, the relative position of individual firms with respect to one another affects both strategy and behavior. Despite the importance of such NETWORKs little is known about relationship patterns and structural complexity of them. Social NETWORK ANALYSIS (SNA) approach is a proper method for modelling and understanding structural attributes of such NETWORKs. Hence, this study identifies those centrality measures of SNA highly related with supply NETWORK efficiency, using buyer-supplier relationship DATA collected from 45 steel companies in Iran. In a multistage procedure, a DEA model (BCC-input oriented) is used to calculate firm level efficiencies and SNA is applied to measure centrality metrics in supply NETWORK. Then, these variables are applied in DATA panel regression with constant effects to identify those variables most relevant to productive efficiency. The results indicated productive efficiency has positive association with in-degree and betweenness centrality and negative association with out-degree centrality in logistic flow NETWORK. Also, in the contractual relationship NETWORK, productive efficiency has positive association with degree centrality and negative association with eigenvector Researches in the literature have confirmed although not without exceptions