The present study is an attempt towards remodeling cost, revenue and profit relationship within the NETWORK process. The previous models of Data Envelopment Analysis (DEA) have been too general in their scope and focused on the input and the output within a black box system, therefore they have not been able to measure various phases simultaneously within a NETWORK system. By using these models internal linking activities are neglected. A slacks-based NETWORK DEA model is DEAlt with intermediate products (Tone, Tsutsui). In this development, each input and output can use situations where unit price and unit cost information are available. In this paper we introduce models of cost, revenue and profit efficiency in NETWORK DEA. These models are illustrated by numerical examples and finally results of Constant Returns to Scale (CRS) are obtained. The findings could be used in minimizing the costs and maximizing the benefits in various organizations, public services, factories, and public and private sector companies.

Efficiency evaluation is an important and key issue in competitive conditions. Organizations and companies face various uncertainties which make their efficiency evaluation extremely difficult and complicated. In this research, open-NETWORK data envelopment analysis models have been developed for three uncertain states, including uncertain outputs, uncertain inputs, and uncertain concurrent inputs and outputs. The proposed models are used to evaluate the efficiency of 10 two-stage processes for sellers and buyers in a supply chain, and the effect of data uncertainty is investigated. The results obtained from the developed models have been compared with the results of traditional DEA NETWORK models. The validity and accuracy of the developed models have also been examined. The results show that the proposed models are more reliable than the traditional DEA NETWORK models. Also, by examining the efficiency of decision-making units in different conditions of data uncertainty and deviations, it was determined that with increasing uncertainty and deviation, the efficiency score of different units decreases, which is consistent with reality.

In data envelopment analysis (DEA), multiplier and envelopment CCR models evaluate the decision-making units (DMUs) under optimal conditions. Therefore, the best prices are allocated to the inputs and outputs. Thus, if a given DMU was not efficient under optimal conditions, it would not be considered efficient by any other models. In the current study, using common weights in DEA, a number of decision-making units are evaluated under the same conditions, and a number of two-stage NETWORK DEA models are proposed within the framework of multi-objective linear programming (MOLP) for finding common weights. Furthermore, using the infinity norm, common weight sets are determined in two-stage NETWORK models with MOLP structures.

Standard Data Envelopment Analysis (DEA) supposes that the performance measure status from the point of view of input or output is known. Nevertheless, in some situations, determining the status of a performance measure in two-stage NETWORK is not easy. Measures with unknown status of input/output are called exible measures. In all of the previous studies did not point to classify exible measures in two-stage NETWORK DEA. In this paper we propose FNDEA models based on the multiplier model under constant returns to scale (CRS) and variable returns to scale (VRS) in general two-stage NETWORK structures. Our models classify exible measures, in which each one of the exible measure is treated as either input or output to maximize the overall NETWORK e, ciency of the DMU under evaluation. The current paper develops an additive e, ciency decomposition approach wherein the overall e, ciency is expressed as a (weighted) sum of the e, ciencies of the individual stages. This approach can be applied under both CRS and (VRS) assumptions. Numerical examples are used to illustrate the procedures.

The paper DEAls with Data Envelopment Analysis (DEA) and Artificial Neural NETWORK (ANN). We believe that solving for the DEA efficiency measure, simultaneously with neural NETWORK model, provides a promising rich approach to optimal solution. In this paper, a new neural NETWORK model is used to estimate the inefficiency of DMUs in large datasets.

Regarding the high importance of university in the growth and development of a country, the e, ciency of educational and research groups in universities is a vital consideration. The black box Data Envelopment Analysis (DEA) model is mathematical programming for measuring the relative e, ciency of a set of Decision-Making Units (DMUs) without considering the operations of the component processes that may have misleading results. To overcome this problem, NETWORK models are recommended. This paper intends to propose a hybrid Intuitionistic Fuzzy Analytic NETWORK Process (IFANP) and NETWORK DEA (NDEA) technique to evaluate the e, ciency of the Faculty of Basic Sciences of Islamic Azad University. IFANP was used to evaluate the overall weights among all the criteria and sub-criteria and the weights were in turn used in the NDEA model to measure the relative e, ciency. A hypothetical example showed that the e, ciency of all DMUs was equal to 1 by using the DEA and there was no ranking among the DMUs. The results of the IFANP-NDEA could be more meaningful with full ranking of the DMUs considering the component process operations. Finally, the model could prioritize the e, cient DMUs and determine the e, ciencies of the DMUs' functions. This model enables managers to identify the areas of weakness in the subject under their study.

In many organizations and financial institutions, it is in many cases more cost and time efficient to access ratio data. Therefore, it is of great importance to evaluate the performance of decision-making units (DMUs) which only have access to ratios of inputs to outputs or vice versa (for instance, ratio of employees to students, ratio of assets to liabilities and ratio of doctors to patients). In this paper, we will propose two-stage NETWORK DEA-R model with multi-objective linear programming (MOLP) structures. Then, introducing a production possibility set (PPS) in each NETWORK stage, we will compare efficiency values in NETWORK DEA and DEA-R. In the end, through an applied study on 22 medical centers which treat special patients in three stages, we will suggest an output-oriented multi-stage NETWORK DEA-R model under assumption of CRS technology. The medical centers are evaluated in all three stages based on overall NETWORK efficiency. The results of the analysis are presented and a future research in this field is discussed in the final section of the paper.

In the present time, evaluating the performance of banks is one of the important subjects for societies and the bank managers who want to expand the scope of their operation. One of the non-parametric approaches for evaluating efficiency is data envelopment analysis (DEA). By a mathematical programming model, DEA provides an estimation of efficiency surfaces. A major problem faced by DEA is that the frontier calculated by DEA may be slightly distorted if the data is affected by statistical noises. In recent years, using the neural NETWORKs is a powerful non-parametric approach for modeling the nonlinear relations in a wide variety of decision making applications. The radial basis function neural NETWORKs (RBFNN) have proved significantly beneficial in the evaluation and assessment of complex systems. Clustering is a method by which a large set of data is grouped into clusters of smaller sets of similar data. In this paper, we proposed RBFNN with the K-means clustering method for the efficiency evaluation of a large set of branches for an Iranian bank. This approach leads to an appropriate classification of branches. The results are compared with the conventional DEA results. It is shown that, using the hybrid learning method, the weights of the neural NETWORK are convergent.