data envelopment analysis

this research, with the aim of evaluating the green innovation ecosystem and the way of green science and technology policy of selected European countries in order to ensure that Europe is moving towards minimizing Carbon, holistic sustainable development, and the green, innovative and competitive economy are on the move. This research is trying to provide a set of useful input, mediator and output indicators to determine the model indicating the stages affecting the performance of the green innovation ecosystem of the selected countries by examining the background of similar research. By collecting the available data through data banks, and preparing the ground for mathematical modeling, the mathematical model was designed by combining methods of network data envelopment analysis and game theory with the help of Lingo software to combine relative efficiency in functional areas and create a Calculate the final weighted score to evaluate the performance of the green innovation ecosystem for EU member states. The results indicate that according to the designed conceptual model including four stages of knowledge creation and development, knowledge implementation, financial and economic transformations and environmental protection, with the change in the performance of countries in the stage of financial and economic transformations, efficiency The total number of countries increases significantly;

When two or more organizations seek to enhance their collaboration, they may opt to form strategic alliances. These alliances aim to progress cooperatively toward shared goals through resource sharing, while maintaining their independence. Maturity models are essential tools for aiding manufacturing organizations in developing their partnerships. However, there is a lack of empirical research on creating a strategic alliance maturity model with clear guidelines. Consequently, no existing model effectively measures the maturity of a strategic alliance, particularly one that can address inaccuracies due to human judgment and inherent evaluation uncertainties. This research aims to design a method to assess the maturity level of strategic alliances, providing a better understanding of the current state of cooperation based on strategic alliance maturity criteria.

This research developed a maturity model using fuzzy logic to evaluate the status of a collaboration at a specific point in time. The strategic alliance maturity model, based on fuzzy logic, was created through a clear and precise procedure and a multi-method approach, including literature reviews, interviews, focus groups, and case studies. A weighted fuzzy inference system combined with the fuzzy data envelopment analysis technique was employed to measure maturity levels using specific indicators. Additionally, a genetic algorithm was applied to generate a set of fuzzy rules. Like all maturity models, the one developed in this research consists of two main components: the maturity levels of the strategic alliance and its maturity dimensions.

The research model defines five levels of maturity: ad-hoc, initial, managed, planned, and optimized strategic alliance. A 44-item list of indicators for measuring strategic alliance maturity was compiled from articles, expert interviews, and analysis of successful and unsuccessful alliances. This list was then categorized into 17 criteria across six dimensions. Essential indicators were identified using the content validity ratio technique, and their relative importance was determined through data envelopment analysis. Expert surveys were used to create fuzzy sets for the variables of the fuzzy inference system. Additionally, a set of fuzzy rules was developed by examining examples of strategic alliances both domestically and internationally, and refining them through expert surveys.

The proposed model has been evaluated and validated through a real case study involving collaboration between a manufacturing organization and its business partner. The research results demonstrate that this approach offers a robust and practical diagnostic tool based on a set of strategic alliance maturity indicators. By analyzing the gaps identified by this model, an action plan can be devised to enhance the maturity level of the strategic alliance.

In this paper, a mathematical modeling is presented to determine efficient locations for deployment of support forces using data envelopment analysis.

The proposed model has the possibility to first change the manageable inputs in order to improve the outputs according to the principle of managerial accessibility and also if it is not possible to reduce the unmanageable inputs according to the principle of natural accessibility, it keeps them at least at the existing level. Therefore, the most important innovation and contribution of the present research is the modeling of the positioning of the support forces to support the ground forces in future battles, which is created by using data envelopment analysis and the simultaneous use of natural and managerial accessibility principles.

The intended model has been used to evaluate 25 potential locations that are ready to provide ground support services to help local forces in the disputed area so that the fire of war ends in favor of local forces. Finally, by providing a sensitivity analysis on the manageable input values, the maximum possible changes on the value of this type of input for each of the locations are specified.

Originality:

 The results of this research are of great importance considering the conditions raised in making and the effectiveness of commanders' decisions in order to locate support forces in future battles.

Data Envelopment Analysis (DEA) is an approach based on mathematical programming for the relative evaluation of decision-making units treated as similar yet distinct production systems. In this approach, the performance of each unit is characterized by describing the transformation of specific inputs into specific outputs. Traditional DEA models assume that the role of each performance factor is clearly defined. However, in some real-world problems, certain factors might be identified as dual-role factors depending on the evaluation nature or the decision-makers' perspective. These factors can play the role of both input and output, or even be considered neutral in assessing the units' performance. In the current paper, to determine the status of dual-role factors and calculate the efficiency of DMUs, two new linear programming models, based on the concept of deviation in the efficiency constraint and a common set of weights, are suggested. The main advantages of the proposed models are significantly reducing the computations and iterations required to solve the model, and involving all DMUs to determine the role of factors. To assess the performance of the proposed models, a data set for the evaluation of eighteen suppliers in the presence of two inputs, three outputs, and two dual-role factors has been employed. The obtained results showed that, compared to other models, the proposed models are computationally more efficient, and the role determination and evaluation of the units, based on the obtained weights from these models, are better aligned with the expectations of decision-makers

Since the healthcare system is one of the most important pillars of community health, and considering that providing healthcare services to the people is one of the elements of individual development in any country, attention and supervision of this sector can lead to development and social welfare. To ensure better and higher quality healthcare services, performance evaluation in the health sector plays a crucial role. In order to achieve this, proper and proportional use of existing facilities and assets is inevitable. In this study, by introducing an application in the field of healthcare systems, the educational hospitals of the country have been measured in terms of performance and their managerial ability has been calculated. Additionally, by identifying and introducing the impact of contextual variables on the performance of decision-making units, their efficiency has been assessed. For this purpose, data related to educational hospitals in 31 provinces of the country was collected, and then by identifying contextual variables and with the presence of undesirable factors, the efficiency was evaluated and the managerial ability of each was calculated. To reach this goal, in the first step, technical efficiency with the presence of undesirable factors was calculated using data envelopment analysis technique, and then the logarithm of technical efficiency obtained from the first stage was regressed on a set of contextual variables that affect hospital performance. In the next stage, managerial ability was extracted from the residual of the regression obtained from the previous stage. Finally, a unique ranking based on the managerial ability of each unit was provided. Ultimately, the results obtained were analyzed and examined in order to provide valuable suggestions for managers and more efficient management of the country's hospitals to maintain public health. According to the study, without considering contextual variables, 25 effective units were evaluated, but by applying the effect of contextual variables on the efficiency index, no unit becomes effective, proving the high impact of such indices on the performance of units. Additionally, in the ranking of units based on managerial ability, Lorestan province ranked first and Golestan province ranked last.

One of the important issues that has attracted the attention of economists over the past decades is the role of financial systems in the economic growth of countries. Insurance industry is one significant component of financial markets. Therefore, the stability and efficiency of insurance industry as a financial market sector is important for a country's economy. One of the most crucial changes in Iran's insurance industry is the regulations having been made in recent years. One main goal of regulation is to eliminate problems of market failure and improve efficiency. This research investigates the effect of these regulations on the efficiency of insurance companies in Iran.

The studied community is 27 insurance companies in the country and the relevant statistics were collected for the years 1385-1400 Persian Year (2005-2020). This study was conducted in two stages. In the first stage, the efficiency of insurance companies has been measured, using the method of data envelopment analysis (DEA) with the bootstrap approach. The implicit assumption of the traditional data envelopment analysis method is that the inputs and outputs are definite. But the output of an insurer is not necessarily certain. To solve this problem, we can use the bootstrap approach to obtain statistical characteristics for this technique. In the second stage, in order to investigate the effect of regulation on efficiency, according to the reviewed studies, the purpose of the present study, and the conditions of Iran's insurance industry, the effective factors on efficiency are determined and the regression model is defined. Then, the dynamic regression model was estimated using the generalized method of moments (GMM) and the related tests confirmed the validity of the results of the model. To calculate the efficiency and estimation of the model, MATLAB and Eviews software were used.

The most important finding of the research is the significant effect of regulation on efficiency. Also the coefficient of regulation, market share, and privateness of insurance companies are estimated as -0.066, 0.041 and 0.306 respectively.

The results of the research show that regulation had a negative effect on the efficiency of insurers and led to a decrease in their efficiency, but the market share and the privateness of insurance companies had a positive effect on the efficiency. In the end, suggestions for improving the efficiency of insurers have been presented.

One of the models that is used to measure the efficiency and decision support is DEA, but considering all the merits, it also has its own limitations, which has led to the idea of combining it with artificial neural networks. ANN is increasingly used in model and data-based approaches to enrich analytical and predictive capabilities and thus improve decision-making. The present research presents a model for evaluating the efficiency of units by integrating neural networks. The studied sector is the active pharmaceutical industry in the Tehran stock market. To create the model, the efficiency of 4 DEA models on a variable scale, including input-oriented and output-oriented BCC model, SBM model and RAM model during the years 2018 to 2022 was calculated in GAMS. The efficiency values of these four models were ANN'S education vector. Also cost, income and profit data from 2018 to 2022 was entered into MATLAB as ANN'S input. To generalize education, the data of 2023 was used. The results showed that the trained efficiency boundary shows a more comprehensive and accurate approximation of efficiency for the ranking of pharmaceutical companies. The results of this research will help pharmaceutical companies in the fields of investment, resource allocation, predicting the results of policies and planning.

The purpose of this study is to provide operational strategies during the Khoy earthquake in the humanitarian supply chain in order to maintain relief and help the victims and meet their needs quickly.The above research method is qualitative.The present research is applied research in terms of purpose and survey-descriptive in terms of nature.The statistical population of this research is the victims and crisis management experts. In this research, firstly, the needs of victims and the strategies to meet these needs were identified by reviewing the literature and conducting semi-structured interviews.Then, the needs of the victims through BMW and humanitarian supply chain strategies through combined approach of QFD and DEA according to Limitations of cost, time, urgency, effectiveness and ease of implementation were prioritized by distributing questionnaires among them.The final findings of the study showed that the strategy of “creating an accident command and crisis management system to plan, coordinate and assign tasks between organizations and institutions involved in the earthquake crisis in order to enable and expedite rescue operations”and“continuous evaluation for continuous improvement in rescue operations and various measures” were the most important strategy, and the need for "relief for the injured" was the most important need for the injured.Therefore, it is recommended to the authorities in the field of crisis management, by setting up the incident command post and the crisis management system, timely response, speeding up rescue operations, preventing parallel actions, creating unity of action and also regular evaluations cause continuous improvement in the process of rescue operations.

Profitability is a crucial indicator of insurance company performance, as it reflects their ability to invest and grow. Supervisors also rely on financial characteristics, including profitability, to determine the viability of insurers. The data envelopment analysis (DEA) method has been widely used to evaluate the performance of insurance companies. However, conventional financial ratios are often lacking in such studies, making DEA an effective alternative for measuring profitability. Given the rapid growth of the insurance industry in Iran, this research aims to examine the profitability of non-life insurance companies in Iran and expand coverage analysis. The study focuses on 18 insurance companies listed on the Tehran Stock Exchange from 2013 to 2014, with complete and available information during the research period. Profitability is measured using DEA, and the Tobit estimator model is used to investigate the impact of company size, company age, and product variety on profitability. The results highlight the importance of properly managing expenses and incomes for insurers. Additionally, the study finds that company size, company age, and product variety do not have a significant relationship with profitability. Insurance companies need to effectively organize their expenses and various types of business to maximize profit ratios. Optimizing cost structures and diversifying business ventures are key strategies for achieving profitability. This research provides insights into insurance company performance, allowing for a better understanding of their relative profitability within the industry and the formulation of targeted strategies. It also aids the government in developing policies that support the industry's growth.

The utilization of data envelopment analysis models for assessing and ranking organizational performance is on the rise. One of the important challenges of this model is the diminishing of the decision-making unit’s precision when dealing with a multitude of inputs and outputs. Hence, the aim of the present research was to develop a comprehensive performance evaluation model while enhancing the resolution of decision-making units. To this end, a balanced scorecard was used to identify comprehensive indicators. At the same time, for the first time, two objective and subjective approaches based on factor analysis and fuzzy inference system were used simultaneously to reduce indicators and improve the resolution of decision-making units. This study used an explanatory-descriptive method and was conducted as an applied-developmental research. The statistical population for identifying performance evaluation indicators and developing fuzzy inference rules included the experts of higher education institutions of Semnan city. Moreover, twenty-four higher education institutions of Semnan city were selected for model testing. The researcher made two questionnaires for the data collection. The validity of the questionnaires was confirmed by content and construct validity, respectively. Also, the reliability of the questionnaires was confirmed by Cronbach's alpha value and composite reliability of more than 0.7 respectively. The main accomplishment of the research can be designing a unified model with objective and subjective approaches to improve the resolution of decision units. In this regard, 26 indicators were identified and reduced to 8 structures by factor analysis. Also, the structures were scored by relying on the designed fuzzy inference system. The results demonstrated a significant improvement in the resolution of decision-making units when utilizing the proposed model, in contrast to conventional models which are mostly based on objective and subjective methods. As a result, the number of effective units in the proposed model effectively reduced to 10. Additionally, the results of the Kruskal-Wallis test and the calculation of the standard deviation of the efficiency scores revealed that the proposed model with an average rating of 48.29 and a dispersion of 0.221 has a lower efficiency rating and a greater dispersion as compered to other models. This finding serves as a confirmation of the enhanced resolution achieved by the proposed model

The electricity industry plays a pivotal role in a country's economic growth and development. Therefore, it is imperative to assess its performance and identify the strengths and weaknesses of its different sectors, such as production, transmission, and distribution, to enhance economic growth in diverse areas. Given the significance of the transmission sector, this research focuses on analyzing and evaluating the performance of 16 regional electricity companies in Iran from 1390 to 1398, with the aim of comprehending the impact of contextual variables on efficiency. To achieve this, the study will utilize two techniques - Data Envelopment Analysis (DEA) and Ordinary Least Squares (OLS) - to determine the efficiency score and estimate the effect of contextual variables on efficiency, respectively. In the first stage, the DEA technique is employed to calculate the technical efficiency of each company, considering their specific inputs and outputs. In the second stage, the logarithm of the efficiency scores obtained is regressed on contextual variables to establish their effect on efficiency. The residual derived from the regression is referred to as managerial ability. Finally, the companies are ranked based on their modified efficiency after removing the impact of contextual variables.

Outsourcing has been the most common method of cooperation. Today, issues such as increased competition, limited resources, technological complications, uncertainty about the future and increased costs have caused organizations to reconsider their management model and turn to new strategies. In such a situation, strategic alliances can replace outsourcing. The purpose of this article is to design a model to evaluate the current state of an outsourcing and to investigate the possibility of developing it into a strategic alliance. For this purpose, in this research, by studying the literature on the subject, examining examples of strategic alliances and also interviewing experts, a list of effective criteria on the formation of a strategic alliance was prepared and categorized into 6 dimensions. Then, using the content validity ratio method, unnecessary criteria were identified in this list. After that, using fuzzy data envelopment analysis without explicit input method, the best combination of weights for essential criteria was obtained. In the next step, by specifying the minimum values for the formation of a strategic alliance for each of the criteria and dimensions of the model, it is possible to compare the current state of the contractor with the minimum value necessary for the formation of a strategic alliance. Results indicates that through this comparison, organizations can identify their weak points in order to become a strategic ally and plan to improve these criteria and, as a result, succeed in developing strategic allies.

Today, productivity is one of the most important factors in economic growth. At the organizational level, a high level of productivity indicates optimal performance to gain a competitive advantage. Despite the important role of productivity of service organizations in economic growth, few studies have been conducted in this context. The purpose of this study is to evaluate service productivity by a combined fuzzy best-worst method and data envelopment analysis.
In the first stage, carrying out a comprehensive review of the research literature, indicators of service productivity evaluation were identified. Then, the evaluation indicators were weighed using the fuzzy best-worst method. After weighing the indicators and determining the importance of each of them, using the data envelopment analysis approach, the efficiency, effectiveness, and productivity of Mazandaran Electricity Distribution Company were evaluated for five consecutive years from 2016 to 2020.
In this study, efficiency, effectiveness, and productivity were simultaneously evaluated using quantitative and qualitative indicators affecting the evaluation of the productivity of electricity distribution companies.
The obtained results would help to assess service productivity by emphasizing both quantitative and qualitative aspects of services and paying attention to the dimensions of efficiency and effectiveness simultaneously. Productivity improvement strategies were also suggested based on the achieved results of the current study.

In this paper, we study a production system that is subject to network failures and produces perishable goods. We assume that the system has preventive and corrective maintenance activities and can return defective items for rework. Our objective is to find the optimal production rate that minimizes the total cost of production, inventory, spoilage, and maintenance over a long planning horizon. We consider the uncertainty of machine failures and use discrete event simulation and ARENA.14 software to estimate the performance measures of the system. We also use data envelopment analysis to evaluate the efficiency of the system and identify the best scenario. The results show the effectiveness of our proposed model.