multiple-criteria decision-making
Sara Asili; Ebrahim Teimoury
Abstract
Considering the competitive environment between suppliers, the issue of choosing them based on important criteria is very important for decision makers, especially in the LARS supply chain, which is a combination of sustainable and LARG supply chains. The aim of this paper is to present a multi-objective ...
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Considering the competitive environment between suppliers, the issue of choosing them based on important criteria is very important for decision makers, especially in the LARS supply chain, which is a combination of sustainable and LARG supply chains. The aim of this paper is to present a multi-objective mathematical model for selecting suppliers based on criteria related to the LARS supply chain concepts. The innovations of the presented model include simultaneous consideration of multiple objectives, multiple periods, multiple products, and master production schedule. The quality of the presented model has been examined on a case study in the country's automotive industry. In this paper, at first, the most important criteria have been extracted from the literature, then finalized by experts in the country's automotive industry, and DEMATEL approach has been used to examine the internal relationships of the criteria in each category of criteria. Secondly, the network of criteria is determined, the importance of each criterion relative to the others was determined using a pairwise comparison matrix and considered as input to the Super Decision software. At last, a mathematical model for optimal supplier selection is presented. Based on the results obtained, the ordering cost had the greatest impact on the objective function. Also, considering the concept of backlog demand has led to flexibility in production volume and, as a result, reduced overall costs.IntroductionLARG Supply Chain Management represents an integrated approach that combines lean, agile, resilient, and green paradigms in supplier relationship management. This holistic framework enables organizations to simultaneously leverage the advantages of each approach while compensating for their inherent limitations. For example, lean supply chain management focuses on minimizing inventory levels to reduce waste and cost; agile supply chain management emphasizes responsiveness and flexibility to meet dynamic market demands; resilient supply chain management enhances the ability to withstand and recover from disruptions; and green supply chain management aims to minimize environmental impacts and promote sustainability. According to Babaei et al. (2017), in an increasingly volatile and uncertain global environment, organizations are placing greater emphasis on supply chain resilience as a key capability for survival and competitiveness. Resilient supply chains are characterized by their capacity to absorb shocks and maintain operational continuity. Meanwhile, the concept of sustainable supply chain management—which addresses environmental, social, and economic concerns—has attracted growing interest among both academics and practitioners. More recently, researchers have begun to explore the LARS framework as a comprehensive model that overlaps with sustainability initiatives, particularly through its emphasis on green practices. However, despite their similarities, LARS and sustainability are distinct in scope and application. This study aims to identify a comprehensive set of criteria for supplier selection under the LARS framework, thereby supporting more informed and strategic decision-making in supply chain management. To this end, relevant criteria will be extracted through expert input from a leading firm in the Iranian automotive industry. In parallel, an extensive literature review will be conducted to incorporate practical and validated indicators associated with lean, agile, resilient, and sustainable supply chain practices. A Likert-scale-based questionnaire will be developed to assess suppliers against these criteria, and the resulting scores will serve as input for a multi-objective mathematical model.The remainder of this paper is structured as follows: Section 2 presents a review of the relevant literature. Section 3 introduces the proposed mathematical model. Section 4 details the case study and analyzes the results. Finally, Section 5 concludes the paper with key findings and managerial implications.MethodsIn this study, the most critical criteria were initially extracted through a thorough review of the existing literature and subsequently refined based on expert insights from the Iranian automotive industry, with a case study focused on Zamiad Company. To analyze the interrelationships among the criteria within each category, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was employed. Following the identification of the criteria interaction network, the relative importance of each criterion with respect to the others was determined using pairwise comparison matrices. These comparisons served as input data for the Super Decisions software, facilitating the application of the Analytic Network Process (ANP). In the next phase of the study, a mathematical model was developed to support the optimal selection of suppliers.Discussion and resultsIn this research, the required data for all ten products were collected from the internal information systems of Zamiad Company. All collected data were evaluated in terms of consistency ratio, which was confirmed as being below 0.1, ensuring the reliability of the pairwise comparisons. After entering the data into Super Decisions software, the output—including the weights of each supplier for each product—were obtained. The results were reviewed and validated by experts at Zamiad Company.Although the proposed model is inherently nonlinear, it was linearized to reduce computational complexity. This adjustment significantly decreased the solution time, which is crucial when solving large-scale problems.The developed mathematical model aims to optimize the selection of suppliers for each product in each production period and to determine the allocated quantity to each supplier. The model incorporates various cost factors, including production, ordering, inventory holding, product shortage, and supplier switching costs. While the parameters of the model were intended to reflect actual company data, due to the confidentiality policies of Zamiad Company, some parameters were not accessible to the researchers. Therefore, these values were generated using normal distributions within predefined intervals based on expert judgment.The model was implemented over two six-month production periods, in alignment with Zamiad’s typical contractual arrangements with its suppliers. Based on the obtained results, ordering cost had the greatest impact on the objective function. Moreover, considering the concept of backlog demand has introduced flexibility in production volume, thereby reducing the overall costs.ConclusionThis study aimed to identify key criteria affecting supplier selection and to determine their relative importance within the integrated LARS supply chain approach. Based on the obtained results, incorporating the production planning process reduces the diversity of suppliers across different periods. This can be attributed to production integration and the model’s preference for maintaining existing contracts over frequent changes. Given the current economic conditions in the production environment, it is essential to consider all relevant parameters in the supplier selection process simultaneously. The findings indicate that accounting for production planning within the LARS framework leads to more effective supplier selection. Among the evaluated parameters, production cost and product ordering cost had a greater impact on the overall performance of the proposed model compared to other factors. Therefore, managerial strategies should focus on controlling these key cost components. This research has contributed by identifying and evaluating these critical parameters.
Industrial management
Sara Bagherzadeh Rahmani; Javad Rezaeian; Ahmad Ebrahimi
Abstract
In today’s project-based organizations, where multiple projects are executed concurrently within work teams, human resources play a crucial role in the success or failure of these organizations. Consequently, human resources are recognized as one of the most essential resources for these organizations, ...
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In today’s project-based organizations, where multiple projects are executed concurrently within work teams, human resources play a crucial role in the success or failure of these organizations. Consequently, human resources are recognized as one of the most essential resources for these organizations, and their optimization can significantly increase productivity while reducing organizational time and costs. This underscores the importance of effective human resource management and highlights the need for special attention to this area. Therefore, this study presents a mixed-integer nonlinear programming model for the multi-objective project scheduling problem with resource constraints, multi-skilled personnel allocation and the assignment of projects to work teams. The mathematical model of this research includes the multiple objectives of simultaneous minimization of the total costs of setting up work teams and the use of human resources and the total flow time of projects. To make the model more realistic, the effect of learning is also considered. Subsequently, a diverse set of test problems at varying scales was designed. Then, the Multi-Objective Artificial Immune System (MOAIS) algorithm and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) were utilized to solve the problems. The results demonstrate the superior performance of the NSGA-II algorithm compared to the MOAIS algorithm.
Introduction
Human resource management is one of the fundamental pillars of organizational success and, alongside financial and technological resources, plays a crucial role in process optimization and achieving strategic objectives. Optimal workforce allocation and effective project scheduling enhance productivity, reduce costs, and ensure the efficient utilization of resources. Teamwork and knowledge sharing facilitate learning and skill development, which, in complex projects with limited resources, lead to shorter project completion times and improved organizational efficiency.
Accordingly, this study addresses the project scheduling problem by considering human resource constraints, multi-skilled work teams, setup times, varying project start times, and work-team dependent learning effect. The primary objective is to simultaneously minimize the total costs of setting up work teams and the use of human resources, as well as the total project flow time. To achieve this, a Mixed-Integer Linear Programming (MILP) model is developed.
Given that the problem is NP-hard, employing metaheuristic algorithms is essential for obtaining near-optimal solutions within a reasonable computational time. This research utilizes two metaheuristic algorithms, NSGA-II and MOAIS.
The findings of this study provide valuable insights for project managers and decision-makers, aiding in optimized project scheduling, efficient workforce allocation, and enhanced organizational productivity.
Research Background
In this section, we mention only a few of the most relevant studies to the current one. Su et al. (2021) explored team formation in project scheduling and presented a simple mathematical model for task scheduling in single-skilled workgroups with restricted access to resources, aiming to minimize makespan (Cmax). In their model, workers were assigned to fixed groups, and tasks were allocated based on processing time and workforce availability. They proposed a hybrid genetic algorithm with a bin-packing strategy to solve the problem.
Mozhdehi et al. (2024) developed a mixed-integer mathematical model for multi-project scheduling with limited resources and multi-skilled workforce. They considered workforce agility, which improves either through collaborative teamwork and knowledge-sharing with more skilled colleagues or by dedicating more time to skill development. Their results indicated that incorporating workforce agility into project scheduling models significantly reduces project completion time.
Methods
In this study, a Mixed-Integer Linear Programming (MILP) model was developed to address the multi-project scheduling problem with multi-skilled work teams. The model integrates human resource constraints, setup times, and work-team dependent learning effect, ensuring a practical and efficient scheduling framework.
For solving the model, the single-objective version was first handled using the Branch and Bound algorithm in Lingo software. Then, for the multi-objective version, two metaheuristic algorithms, NSGA-II and MOAIS, were implemented to generate high-quality trade-off solutions.
To assess and compare the performance of these algorithms, a set of test problems of different scales (small, medium, and large) was designed and solved. The Taguchi Experimental Design Method was employed to fine-tune the key algorithm parameters, optimizing efficiency and accuracy.
Evaluating the performance of multi-objective metaheuristic algorithms is more complex than that of single-objective optimization due to the presence of non-dominated solutions that cannot be strictly ranked. In this study, the following key metrics were used to assess solution quality and diversity:
Number of Pareto Solutions (NPS)
Mean Ideal Distance (MID)
Diversity Metric (DM)
Spread of Non-dominated Solutions (SNS)
Discussion and Results
Results of sensitivity analysis reveals that increasing the learning rate of work teams significantly reduces project completion time. This finding underscores the importance of incorporating learning effects in multi-skilled workforce scheduling models. With a higher learning rate, teams execute tasks more efficiently and in less time, directly contributing to organizational productivity improvements.
Furthermore, computational results indicate that in small to medium-sized problems, there is no significant performance difference between NSGA-II and MOAIS. However, in large-scale problems, NSGA-II outperforms MOAIS. This superiority is attributed to NSGA-II’s population evolution mechanism, which enables a broader exploration of the solution space and prevents premature convergence to local optima. In contrast, MOAIS, due to its elitist nature, primarily focuses on replicating high-quality solutions, avoiding exploration in other regions of the search space. This increases the likelihood of getting trapped in local optima, thereby reducing search diversity. Furthermore, performance comparison results indicate that NSGA-II demonstrates superior Pareto front coverage and convergence to optimal solutions compared to MOAIS.
Conclusion
This study investigated the project scheduling problem considering human resource constraints, multi-skilled work teams, setup times, varying project start times, and work-team dependent learning effects. The primary objective was the simultaneous minimization of the total costs of setting up work teams and the use of human resources and the total flowtime of projects. To achieve this, a Mixed-Integer Linear Programming (MILP) model was developed, and its performance was evaluated through sensitivity analyses and numerical experiments. The results demonstrated that the proposed model performed effectively under various constraints and exhibited high accuracy and efficiency.
Given the NP-hard nature and multi-objective characteristics of the problem, two metaheuristic algorithms, NSGA-II and MOAIS, were implemented to solve it. The algorithm parameters were fine-tuned using the Taguchi Experimental Design Method, and their performance was compared across different problem sizes. Computational results indicated that while both algorithms performed similarly in small to medium-sized problems, NSGA-II outperformed MOAIS in large-scale instances. Further analysis revealed that MOAIS, due to its elitist-based nature, primarily focuses on replicating high-quality solutions, often avoiding broader exploration within the solution space. This characteristic increases the likelihood of getting trapped in local optima, reducing solution diversity. In contrast, NSGA-II, through its non-dominated sorting mechanism, allows lower-fitness solutions to participate in the evolution process, leading to broader solution space exploration and preventing premature convergence.
For future research, it is recommended to extend the model by incorporating additional operational assumptions, such as activity failure probabilities, simultaneous consideration of learning and forgetting effects, rework processes, and uncertainty of certain parameters in the problem. Furthermore, exploring more advanced heuristic and hybrid metaheuristic algorithms is suggested to enhance the efficiency of the solution approach.
Industrial management
Mohsen Keramatpanah; Mohammad Taghi Taghavifard; Maghsoud Amiri; Mehdi Mehdi Shamizanjani
Abstract
With the expansion of digital technologies and fundamental changes in the financial services ecosystem, the banking industry is facing new challenges on the path toward sustainable development. In this regard, the present study aims to propose a model for achieving sustainability in digital banking within ...
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With the expansion of digital technologies and fundamental changes in the financial services ecosystem, the banking industry is facing new challenges on the path toward sustainable development. In this regard, the present study aims to propose a model for achieving sustainability in digital banking within Iran’s private banking sector. To this end, key variables were initially identified through a review of the relevant literature and subsequently validated using the fuzzy Delphi method with input from subject matter experts. To analyze the relationships among the variables and determine their systemic structure, Interpretive Structural Modeling (ISM) was employed. Subsequently, a comprehensive digital banking sustainability model was developed and simulated using a system dynamics approach and related software tools. The results of the model analysis indicate that the development of technological infrastructure, enhancement of information security, improvement of customer experience, effective collaboration with regulatory bodies, and standardization of digital processes are key drivers in realizing sustainability in digital banking. Scenario analysis further reveals that simultaneous improvement of these components, by reinforcing positive feedback loops, can lead to sustainable growth and lasting competitive advantage for private banks. In this context, attention to sustainability requirements in digital banking and the development of related models can contribute to reducing operational costs in the banking network, optimizing energy consumption, increasing financial inclusion, and delivering more efficient services, thereby facilitating sustainable development. The primary contribution of this study is the development of an analytical and practical framework based on system dynamics modeling, which can support private banks in formulating strategies for sustainable digital transformation and serve as a roadmap for improving their economic, social, and environmental performance. The findings of this research can assist policymakers, banking executives, and researchers in the digital banking domain to better understand the factors influencing sustainability and to design effective interventions.
Introduction
As the banking industry experiences rapid digitalization driven by emerging technologies and evolving customer expectations, the necessity of aligning digital transformation with sustainability imperatives has become increasingly evident. This shift transcends traditional financial performance and calls for banking institutions to engage with social and environmental responsibilities while sustaining innovation. Particularly in developing economies such as Iran, the dual challenge of digital modernization and sustainable development presents a unique strategic frontier. In response to these dynamics, the present study proposes a comprehensive and dynamic model of sustainable digital banking, focusing on the private banking sector in Iran.
Research Background
While digitalization and sustainability have been widely studied, there exists a significant gap in integrated models that account for the systemic interactions between the two domains. The prevailing literature tends to analyze digital banking from a technological perspective, often overlooking its environmental and social consequences (Del Carmen, 2022; Migliorelli & Dessertine, 2023). Similarly, sustainability-focused frameworks rarely include the disruptive potential of digital technologies such as blockchain, artificial intelligence (AI), and mobile banking. Some studies have suggested the relevance of digital sustainability in areas like waste reduction, financial inclusion, and eco-efficiency (Castro et al., 2021; Di Vaio et al., 2021), yet empirical applications remain sparse. By building on these foundations, this research aims to bridge conceptual and practical gaps by designing a policy-oriented, simulation-based model grounded in a system thinking approach.
Materials and Methods
This study adopted a hybrid methodology by integrating three complementary techniques—Fuzzy Delphi, Interpretive Structural Modeling (ISM), and System Dynamics (SD). Initially, a structured literature review spanning 2016 to 2023 was conducted using scholarly databases such as Scopus and Google Scholar. This led to the identification of 26 preliminary variables, which were refined and validated through expert consensus using the Fuzzy Delphi method. Experts were selected based on their experience in digital transformation initiatives and their roles in IT, innovation, or strategic planning within private banks. The Delphi rounds applied fuzzy logic to quantify agreement levels, ensuring rigor in variable selection. To structure the relationships among the validated variables, ISM was employed. This method facilitated the construction of a hierarchical framework to determine which variables serve as inputs, intermediaries, and outputs in the sustainability process. The final phase involved constructing a system dynamics model with feedback loops and flow diagrams to simulate the behavior of key sustainability indicators over time and under different scenarios. The sustainability model incorporated variables reflecting organizational, technological, social, and environmental dimensions. These include elements such as process improvement, trust, profitability, service quality, digital culture, smart leadership, electronic KYC, stakeholder satisfaction, employee welfare, and business model adaptability. By embedding these interconnected elements into the system structure, the model is designed to capture the complexity of digital sustainability transitions in a banking context.
Data Analysis and Findings
The system dynamics model as shown in Figure (1) was simulated across multiple scenarios to understand how different policy strategies would influence long-term outcomes. The simulation process ensured that model behavior aligned with historical data and logical expectations. Scenario analysis provided valuable insights into how banks can prioritize investments and strategic decisions to balance economic growth with sustainability. For instance, increasing investment in digital infrastructure and employee training led to noticeable gains in trust, customer acquisition, and organizational resilience. Similarly, enhancing authentication systems and E-KYC protocols improved operational efficiency while supporting environmental and social performance through reduced paperwork and increased accessibility. Findings confirmed that sustainability in digital banking is best achieved through a synergistic approach that combines internal cultural transformation with technological innovation and regulatory collaboration. Variables such as digital culture, smart leadership, and business model flexibility played a central role in facilitating systemic resilience. The results emphasize that merely focusing on digital upgrades is insufficient without aligning human capital and governance frameworks with sustainability objectives. Scenario comparisons further revealed that a balanced investment strategy targeting both operational excellence and environmental awareness yields the most robust outcomes.
Figure 1:SD model
Conclusion
The results of this research highlight the imperative for private banks in Iran and similar emerging markets to adopt integrated strategies that harmonize digital transformation with sustainability goals. This study makes a significant contribution by constructing and validating a system dynamics model that incorporates 26 interdependent variables derived from theory and practice, offering a holistic view of the sustainability landscape in digital banking. The model is unique in that it not only captures the technological enablers of sustainable banking but also embeds social and environmental dimensions, reflecting the true complexity of this transformation. The integration of Fuzzy Delphi and ISM methods with SD modeling allows for both structural clarity and dynamic simulation, enabling banks to test and adjust strategies before implementation. By embedding feedback loops and behavioral equations, the model delivers actionable insights into how various factors—such as trust, service quality, organizational agility, and stakeholder alignment—can collectively drive sustainability in the digital era. This comprehensive approach moves beyond linear planning, providing a real-time decision-making tool for managers, regulators, and researchers alike.
Further Research Ideas
Future research can extend the current model by integrating real-time behavioral data through machine learning algorithms or agent-based modeling, offering more granular and predictive insights. Comparative studies across multiple banking systems or geographic regions would also enhance generalizability. In addition, introducing ESG indicators and green finance parameters could further enrich the environmental scope of the model. Finally, longitudinal studies using historical banking data can help validate the dynamic behavior of the system across different economic cycles.
Managerial Suggestions
From a managerial perspective, several strategic recommendations emerge. First, banks should prioritize long-term investment in digital literacy and workforce development to foster a resilient culture aligned with sustainability values. Second, integrating advanced E-KYC and biometric verification tools can enhance customer trust and operational transparency. Third, collaborative governance involving fintechs, third parties, and regulatory bodies is essential for ecosystem resilience. Fourth, it is crucial to continuously monitor and update business models to reflect technological, societal, and environmental shifts. Finally, the adoption of sustainability-oriented performance metrics should be institutionalized to ensure that growth and digitalization are guided by ethical and ecological responsibility
safety,risk and reliability
Reza Masoudi; Mohammad Reza Zahedy; Morteza piri
Abstract
This study aims to enhance the performance of maintenance teams by proposing a mathematical programming model for team formation, focusing on the maximization of knowledge sharing. In this model, key factors influencing knowledge sharing, including knowledge absorptive capacity, knowledge-sharing capability, ...
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This study aims to enhance the performance of maintenance teams by proposing a mathematical programming model for team formation, focusing on the maximization of knowledge sharing. In this model, key factors influencing knowledge sharing, including knowledge absorptive capacity, knowledge-sharing capability, willingness to share knowledge, and motivation to acquire knowledge, along with the required expertise for maintenance activities, are identified and incorporated into the optimization process. The data used to develop the model were collected through performance evaluation questionnaires and expert interviews. The implementation of the proposed model in an organization with extensive physical assets demonstrates that optimizing team formation leads to significant improvements in key performance indicators, such as reducing mean time to repair, increasing equipment availability, and enhancing overall system productivity. The findings of this study indicate that knowledge sharing not only improves individual and team skills, but also plays a key role in improving the performance of maintenance teams. The proposed model can be used as an efficient decision-making tool for maintenance managers in various industries and help them form optimal teams to improve efficiency, reduce operational costs, and enhance equipment reliability.
perfomance management
Maryam Sharifi; Sohrab Kordrostami; Leila Khoshandam
Abstract
In production technology, studying the effect of an indicator on one or more other indicators while maintaining efficiency, under the name of marginal rate, can provide valuable information to managers for better management of the system. In this paper, the aim is to study the effect of meaningful indicators ...
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In production technology, studying the effect of an indicator on one or more other indicators while maintaining efficiency, under the name of marginal rate, can provide valuable information to managers for better management of the system. In this paper, the aim is to study the effect of meaningful indicators on each other and in a specific two-stage structure with the presence of undesirable outputs. In this study, unlike previous studies, production technology is divided into two sub-technologies in a two-stage structure and then, focusing on the application issue, first the effect of a specific input from the first stage on the intermediate indicator is measured and then by calculating the changes made in this indicator, which is calculated by the proposed model, its effect on the specific final output is measured as a transmission factor. In this paper, focusing on data collected from 21 provincial power plants consisting of interdependent "generation" and "transmission" sections, each structural unit has a similar structure to the stated structure. Considering the total technology distribution, the effect of increasing or decreasing the fuel type component is taken as the first stage input on the electricity flow, and then the changes in electricity flow are measured on the total system revenue.
project management
mohammad forozandeh; Amirali Foukerdi; majid salamati
Abstract
Navigating today’s financial markets, which are fraught with uncertainties, necessitates a thorough understanding of the future of venture capital fund management. This requires innovative design and the adoption of new methods for managing knowledge-based projects. One effective approach to overseeing ...
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Navigating today’s financial markets, which are fraught with uncertainties, necessitates a thorough understanding of the future of venture capital fund management. This requires innovative design and the adoption of new methods for managing knowledge-based projects. One effective approach to overseeing various projects across industries is the establishment of a Project Management Office (PMO) as an organizational unit. The aim of this article is to design a suitable implementation framework for the PMO to enhance the management of knowledge-based projects, improve the business and investment ecosystem, and foster strategic partnerships with co-investors—key elements for boosting performance in companies and investment funds. To achieve this, the study analyzes the current maturity and development of the PMO within a fund, identifying the status of related processes and infrastructures from the stakeholders' perspective. Data were collected through interviews with five experts and analyzed using the Organizational Project Management Maturity Model. The findings indicate that establishing an effective PMO framework in a venture capital fund requires a comprehensive analysis of the current situation, assessing maturity levels, and determining starting points for improvement. This process is structured around four main steps: preparing the team and defining the research scope; assessing the current state and measuring maturity; identifying the desired maturity level; and presenting a plan to achieve the organization’s goals along with the necessary solutions.
modeling and simulation
Afshin Aminipour; Akbar Bagheri; Hamidreza Kordlooie
Abstract
The aim of this research is to analyze the impact of banking fintech on the risk levels of selected banks listed on the Tehran Stock Exchange.The statistical population consists of 41 banks and credit institutions listed on the Tehran Stock Exchange, according to the latest report by the Central Bank ...
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The aim of this research is to analyze the impact of banking fintech on the risk levels of selected banks listed on the Tehran Stock Exchange.The statistical population consists of 41 banks and credit institutions listed on the Tehran Stock Exchange, according to the latest report by the Central Bank of the Islamic Republic of Iran in May 2022. Using a systematic elimination method, and covering the period from 2019 to 2022, a sample of 8 banks was chosen. The results of hypothesis testing, based on the first and second models of the study (with Z-Score and business risk of the studied banks as the dependent variables, respectively), showed that in the first model, under the dynamic condition (with lag), the banking fintech variable does not have a significant effect on the risk management index of the selected banks. Additionally, it was found that in the non-lagged model, the fintech innovation variable, through improved operational performance, and the variable of operational revenues, as well as each bank’s share of total transaction values from mobile payment acceptance tools, significantly impact the risk management index. Other findings also showed that in both models, under both lagged and non-lagged conditions, variables related to each bank’s share of transaction values from mobile, POS, and internet payment acceptance tools, as well as fintech innovation variables related to improved operational performance and capital adequacy, do not significantly affect the risk management index of the selected banks listed on the Tehran Stock Exchange.
