In this article, Non radial method of Dynamic DEA with the parallel Network structure is presented and is used for calculation of relative efficiency measures when inputs and outputs do not change equally. In this model, DMU divisions under evaluation have been put together in parallel. But its Dynamic structure is assumed in series. Since in real applications there are undesirable inputs and outputs in the proposed model, the assumption of the existence of the intermediate products have been considered. After obtaining period–divisional efficiencies, by considering its weighted arithmetic mean, models are presented for the evaluation of period, divisional and overall efficiency for decision making unit.

The results of Dynamic efficiency evaluation not only help managers to realize their business' position in competitive market, but also enable them to compare current company’s performance with previous periods and do strategic planning properly. For doing so, Network data envelopment analysis is a logical approach. Hence, the main objective of this illustration is to measure Dynamic efficiency by means of Network data envelopment analysis technique. Although different approaches in Network DEA are introduced recently, the need for a comprehensive methodology in this area is remained because of the defects of previous methodologies. Consequently, a novel approach based on multi-objective optimization is introduced in this paper in order to measure the efficiency of a Network structure. Finally, the case of Machine Made Carpet Industry (MMCI) is used and the Dynamic performance of MMCI's companies in the period of four years is measured. Efficiency results of case data showed that the methodology proposed in this paper is able to eliminate defects of previous approaches and evaluate both total and annual efficiency simultaneously.

This study introduces an advanced performance measurement system for 31 municipalities in Tehran and Shahriar, integrating the Balanced Scorecard (BSC) and Data Envelopment Analysis (DEA) methodologies. The combination of BSC and DEA was chosen because BSC offers a multidimensional framework for assessing performance from diverse perspectives, while DEA provides a quantitative tool for evaluating efficiency, particularly useful when DEAling with multiple inputs and outputs. Together, they allow for both qualitative and quantitative evaluation of municipal performance, addressing the need for comprehensive performance assessment. However, traditional DEA models often fail to account for Dynamic changes and intermediate linkages between these perspectives over time. The Dynamic Network Slacks-Based Model (DNSBM) of DEA, proposed in this study, addresses these limitations by incorporating both Network interdependencies and Dynamic changes in performance evaluation. Field studies and expert interviews revealed interconnections between BSC perspectives, and Dynamic changes were modeled by linking Networks over multiple periods. The model estimated efficiency values for each period, showing an average overall score of 0.857, with specific scores for financial (0.94), learning and growth (0.83), internal processes (0.96), and customer (0.34). Statistically significant correlations were found between most perspectives, except financial and learning/growth. The model identified Dynamic performance trends, inefficiency levels, and strategies to improve underperforming DMUs, offering a comprehensive approach to enhancing municipal performance.

Dynamic systems have always attracted much attention from researchers as a significant part of the various types of systems, and modeling of supply chain processes is considered as one of these due to the nature of its change over time, the volatility of customer demand is considered as one of these problems that have many effects on the system and its costs. In the present study, the SCOR Supply Chain is first modeled with the Dynamic Systems Approach (DSA) under specific parameters. We determine the control parameters of the studied policy using the DEA-SCOR model. We also Improvement the basic five-stage model to investigate models incorporating advanced demand information and evaluate the influence of demand variability on the system performance. Then, the evaluation and ranking of the supply chain Network of several distribution companies have been analyzed using the indicators of information sharing, based on the opinion of managers and experts familiar with the subject and by a combination of the data envelopment analysis (DEA)-SCOR and stochastic frontier analysis (SFA). By calculating total efficiency according to DEA-SCOR model in SCOR supply chain supply Network of oil products Distribution Companies, Falavarjan Branch and Tehran Office Branch, have the highest performance, and the lowest performance is observed in Lordegan, Shahriar branch. According to the results of the SFA method, Tehran Office Branch and Isfahan Branch had the highest performances, and Shahriyar and Tiran branches had the lowest performances. The performance level calculated using the SFA method is approximately the same as the performance level calculated using the DEA method. The performance calculated by the DEA method is less than that calculated by the SFA method in some cases. The average calculated performance in the DEA method equals 0. 80, and the average for the SFA method is 0. 82. Given the inadequacy of indicators and the improvement of these indices at each stage, the calculated efficiency in each Dynamic period gradually improves, and the average total performance in the Dynamic period is 0. 90.

Nowadays the efficiency-o7666riented performance evaluation of complex systems be-comes increasingly important for investment and management decisions. This paper proposes a new formulation approach based on Dynamic Network DEA (DN– DEA) models for multi-period and multi-division (MPMD) systems to measure and decompose the overall efficiency. Although conventional DEA models provide magnificant modeling iDEA of efficiency measures in the multi-input and-output contexts, they do not account for the multi-division transformation process of decision-making units (DMUs) and present “ black-box” measures of their efficiency scores wich causes incorrect estimates of the efficiency points of the units by ignoring the information about the internal operations of the system. As an illustrative case study, the present research applies the proposed approach to evaluate the efficiencies of 13 universities of Iran over the three-year period and the rate of innovation has been measured according to each division, every period, as well as total innovation.

In this article, by presenting the scope of national innovation capability in the context of a multi-sector system, a Dynamic Network model is introduced. In this system, to identify Iran's performance problem, at first by bibliometric studying and holding focus group sessions with experts, the steps and indicators of the processed model were identified and designed. Then, the Dynamic Network data envelopment analysis model was implemented to compare Iran's performance with other countries. The model results indicated that Iran's national innovation capability has a poor performance in the third phase of its three steps that include converting patents to hightech products and creative goods. Then, to present the proposed policy to enhance Iran’, s performance in the third step of the mentioned model, by using qualitative comparative analysis of fuzzy set (fSQCA), the combinations of institutional, human capital and research, infrastructure, market sophistication, and business sophistication dimensions were investigated. For calibration of these data, K-MEANS clustering method was used. The output of the mentioned comparative analysis indicated that the combination of the two dimensions of institutions and human capital and research in promoting the country's performance is sufficient.

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.

In this paper, sustainability-related factors involved in the success of healthcare system management including a group of hospitals are considered. A three-pronged approach is adopted based on the internal functions of a hospital which affect such functions as social responsibility and the ones related to the services received from health centers. A novel comprehensive multi-period evaluation of hospital performance is applied to the proposed Dynamic Network. This hybrid data envelopment analysisbased fuzzy multi-criteria decision-making model incorporates fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL), and the best-worst method provides a number of useful managerial insights based on a realistic case study from Iran to ensure sustainable management of the healthcare system. Obtained results indicate the important role of considering potential inter-related relations between Network nodes to ensure a comprehensive performance assessment of the healthcare service system.

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.