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.

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.

One of the most useful tools in Operations Research (OR) which is essentially applied to evaluate the performance of treated Decision-Making Units (DMUs) is Data Envelopment Analysis (DEA). Because of in the current decades, DEA models have been used and extended in many disciplines and hence attracted much interests. The traditional DEA treats DMUs as black boxes and calculates their efficiencies by considering their initial inputs and their final outputs. Since, in the real situations, input Data are included some uncertainties, hence in this study we consider a DEA with fuzzy stochastic Data and suggest a three-stage DEA model by taking into account undesirable output. To achieve this aim, an extended probability approach is applied to the reform of three-stage DEA models. Finally, we give an illustrative example by considering a case study on the banking industry.