https://systems.eyc.ac.ir/ System Engineering and Productivity en daily 1 Mon, 22 Dec 2025 00:00:00 +0330 Mon, 22 Dec 2025 00:00:00 +0330 https://systems.eyc.ac.ir/article_726747.html The housing construction industry in Iran is influenced by a multitude of factors across various dimensions, including social, economic, physical, infrastructural, and legal aspects. To enhance land use efficiency, optimize physical assets and capital, address genuine housing needs, improve the quantity and quality of housing, and protect the environment, it is imperative to adopt the principles of Construction 4.0. This study aims to identify and elucidate the barriers to achieving Construction 4.0 in the Iranian housing sector from the perspectives of key experts and stakeholders. The research employs a qualitative content analysis approach, utilizing data collected through semi-structured, in-depth, face-to-face interviews designed with open-ended questions. The research population consists of academic and technical-experiential experts, with purposive sampling applied to ensure maximum diversity in age, gender, specialization, educational level, professional background (academic and technical), and experience, to obtain comprehensive and rich insights from varied perspectives. The participants comprised 31 experts from diverse fields, including construction management, project management, architecture, civil engineering, environmental science, mechanical engineering, electrical engineering, energy, industrial management, industrial design, and related disciplines. Through data conceptualization, the study identified 5 main themes, 28 sub-themes, and 105 codes. The main themes are: "socio-cultural”, "economic”, "institutional-administrative”, "legal-regulatory”, and "infrastructural”. https://systems.eyc.ac.ir/article_728595.html This study aims to identify and hierarchize barriers to adopting Construction 4.0 in Iran's housing development sector from an innovation perspective, employing an Interpretive Structural Modeling (ISM) approach. To evaluate the influence and dependency of these barriers, insights from 10 experts, selected via purposive snowball sampling, were collected. Data were gathered through a pairwise comparison questionnaire, with the highest level of expert consensus determining the decision-making criterion. The barriers to implementing Construction 4.0 in housing development were classified into seven hierarchical levels, where lower levels represent foundational barriers and higher levels denote influential barriers. Market conditions and energy resources emerged as highly influential barriers, while tradition and past experiences were identified as foundational barriers. Analysis of dependency strength and driving power revealed that "change and acceptance" and "awareness and education" exhibited significant influence and dependency, whereas "innovation and utilization" and "energy" demonstrated comparatively lower impact. https://systems.eyc.ac.ir/article_728153.html Small and medium enterprises (SMEs) encounter significant challenges in digital transformation during various crises, including economic shocks and supply chain disruptions. This research develops an integrated sustainability and resilience model for smart technology implementation in SMEs under crisis conditions. The methodology employs an innovative combination of Real Options Theory (ROT) with Deep Reinforcement Learning (DRL) algorithms. Through an exploratory-analytical study, data from 85 small enterprises were collected and analyzed. Fifteen key criteria affecting the sustainability and resilience of smart technology implementation were identified and categorized into organizational factors, technological characteristics, and environmental factors. Structural equation modeling was utilized to analyze relationships between these criteria and mediating variables of technology absorption capacity and change management capability. Real options modeling was conducted using the binomial tree method, followed by the development of a reinforcement learning model to optimize smart technology implementation strategies. Results demonstrate that smart technologies with high scalability, adaptability, and operational efficiency exhibit greater implementation success during crisis conditions, with Internet of Things achieving the highest adoption rate among examined technologies. The proposed model predicts smart technology implementation success under crisis conditions with 89.2% accuracy. This research provides a decision support system enabling enterprise managers to formulate sustainable and resilient strategies for smart technology implementation under severe uncertainty conditions. https://systems.eyc.ac.ir/article_728596.html This study adopts a systematic approach to identify the key challenges facing Knowledge-Based Companies (KBCs) and provide a comprehensive analysis of the current and desired states across three levels: KBCs, Science and Technology Parks (STPs), and innovation ecosystem actors. To achieve this, a structured questionnaire based on 40 Key challenges was designed and randomly distributed among 58 KBCs located in the STPs of Zanjan Province. Managers' perceptions were collected and analyzed regarding their satisfaction with the current state and the importance of each challenge to their success. A t-test revealed a statistically significant gap between the current and desired states across all three levels. Further analysis indicated that at the KBC level, managers tended to overestimate their own capabilities with more than 85% believing their companies were performing above 70%. In contrast, they rated the current performance of the STPs below 55% and the performance of ecosystem actors below 34%. This highlights a strong external attribute of failure among managers. The main challenges at the STPs level were identified as lack of financial support, and inadequate infrastructure. At the ecosystem level, the leading issues were the unclear roles of responsible organizations (78%) and insufficient cooperation and support from them (74%). In conclusion, the following are recommended: (1) enhancing the awareness and innovative capabilities of companies; (2) improving infrastructure and the governance quality of STPs; and (3) clarifying the roles of different actors and the coordination mechanisms among them. These findings can contribute to improving policymaking quality and strengthening KBCs. https://systems.eyc.ac.ir/article_726467.html Driving is a complex daily activity that requires rapid information processing and decision-making. Due to the potential for human error, this process can pose significant risks. Research indicates that the human factor—the most critical and complex component in the driving safety triangle (vehicle-environment-driver)—profoundly influences driving behavior. This study analyzes factors contributing to the arousal of negative emotions and their impact on driving behavior. Driving often triggers negative emotions such as anger and stress, jeopardizing both mental health and road safety. Using the standardized Manchester Driving Behavior Questionnaire and a custom survey based on literature, data were collected from 436 students at K. N. Toosi University of Technology. Structural equation modeling (SEM) and partial least squares SEM (PLS-SEM) were employed for analysis. Key factors—including age, gender, driving experience, personality traits, and environmental conditions (e.g., weather, lighting, time of day)—were found to significantly influence negative emotions and driving behavior. Results demonstrated high validity and reliability of the proposed model, with Cronbach’s alpha exceeding 0.7. Independent predictor variables showed no multicollinearity. All 11 hypotheses were supported, and the Q² value confirmed the model’s predictive power. By offering critical insights into driving behavior, this study provides a valuable foundation for future research and practical interventions to mitigate driving risks and enhance road safety. https://systems.eyc.ac.ir/article_728639.html In complex, high-pressure, and multi-layered environments such as hospital emergency departments, system inefficiency is not simply due to a lack of resources, but rather the result of the uncoordinated interaction of human, structural, communication, and information components. The present study, with the aim of analyzing the problematic situation in the emergency department and designing the desired change, has used the soft systems methodology as an interpretive, participatory, and human-centered approach. In this study, the seven stages of SSM were fully implemented, from recognizing the current situation and drawing a rich picture, to defining the root system, designing a conceptual model, analyzing gaps, and identifying desirable and feasible changes. The research findings showed that the main functional nodes of the emergency department include: lack of a platform for dialogue and reflection of experiences, opaque structure and unclear division of duties, lengthy decision-making process and administrative bureaucracy, distrust of recorded information and rework, lack of a transparent channel for organizational learning, lack of a performance-based reward system, and lack of meaningfulness of data for stakeholders. In response to these challenges, seven intervention proposals were presented, including: designing the meeting structure, redesigning the admissions process, clearly defining roles and responsibilities, designing training packages, reviewing and modifying electronic documentation forms, setting up rapid feedback stations, and designing and deploying local performance indicators. This model is not only theoretical and generalizable, but also derived from a real organizational context and can be implemented in the field. Overall, the results show that traditional problem-solving approaches, without considering human understanding and systemic interactions, have failed in health-oriented organizations; and only through participation, dialogue, and joint redefinition of the problem can sustainable and effective changes be achieved. https://systems.eyc.ac.ir/article_727486.html The role of sovereign wealth funds in the economic development and growth of countries is such that today the most successful countries in terms of development and growth have the largest sovereign wealth funds. This study aims to measure and improve the risk-taking of the NDFI in the investment scope in the dimensions of: financial health (leverage risk and portfolio risk), quality of facilities (credit risk), and the relative risk-return performance of the fund using the following criteria: Z-Score, NPL, Sharpe ratio, and a hybrid risk-taking (RT) measure. The findings, using the fund's annual financial statement data (from 1390 to 1401) and its processing using Python show that the Z-Score value decreased from 1.40 in 1390 to 1.39 in 1401 (a decrease in the fund's financial health), while NPL improved from 29% to 20% in the same period. The return-risk performance also changed from 1.77 in 1390 to -1.69 in 1401, and it shows vital review on financing high-risk projects. VaR decreased from 2.3 in 1390 to 2.16 in 1401. The fund's RT increased from 0.62 in 1390 to 0.76 in 1400 and decreased to 0.49 in 1401. Also, we estimated RT's improvement using the ARIMA model. https://systems.eyc.ac.ir/article_728672.html The present study was applied in terms of purpose and data collection method, and was a descriptive-correlational survey. The statistical population of management levels and experts of knowledge-based companies in the Science and Technology Park of the University of Tehran was 187 people, of which 126 people were selected using stratified sampling based on the Cochran formula and were measured with a 25-question adapted questionnaire whose validity was confirmed by content and construct methods and its reliability was confirmed by a Cronbach's alpha coefficient of 0.82. Descriptive statistics and SPSS 24 software were used to analyze demographic data and extract central indicators, and structural equation modeling and PLS2 Smart software were used to analyze inferential statistics. In general, in this study, the relationship between the buyer and the supplier was examined in terms of information sharing, information quality, and supply chain performance. The results indicate that supply chain partners can coordinate their activities by providing high-quality information to enable interactions between buyers and suppliers, and that information sharing acts as a mediator between information quality and supply chain performance. In other words, information sharing, as a vital mechanism, plays the role of a communication bridge and enables the full exploitation of high-quality information to achieve superior supply chain performance. The research model with an excellent fit index was able to explain 49.1% of the variations in supply chain performance. https://systems.eyc.ac.ir/article_727923.html The aim of this paper is to investigate and propose a novel mathematical model to enhance the performance of production systems in the petrochemical industry by utilizing a closed-loop supply chain approach. The mathematical model is designed to optimize material flow, procurement, and product distribution within petrochemical systems, and is capable of accommodating uncertain demand. The objective functions considered include transportation and inventory costs (as the first objective function) and the level of pollution at treatment and distribution centers (as the second objective function). The primary goal of this research is to develop a mathematical model that simultaneously manages production costs and environmental pollution within the closed-loop supply chain of petrochemical products. The model seeks to find an optimal balance between costs and the environmental impacts arising from the production and distribution of petrochemical products. The proposed model is solved using the weighted sum method and the NSGA-II metaheuristic algorithm. According to the results obtained for the objective function, it is observed that the value of the objective function is highly sensitive to the weight assigned to the first objective. Increasing this weight leads to an increase in the objective function value, whereas a higher weight for the second objective results in a decrease in the objective function value. The findings indicate that the minimum value of the objective function is 35,996.988. Additionally, the results of the metaheuristic approach demonstrate that as the problem size increases, its computational complexity increases as well. https://systems.eyc.ac.ir/article_728329.html This research aims to design a mathematical-fuzzy model to measure and improve the risk-taking of the National Development Fund of Iran in the field of investment under a project-oriented approach. To this end, by studying previous research and the experiences of fifteen top global sovereign wealth funds, a mathematical model based on the concept of mean-variance and with the goals of maximizing returns at a given level of risk, and minimizing risk at a given level of return, has been presented and implemented. The findings of solving the model in Python using the initial rate of return of 318 projects financed/participated in by the fund from the beginning of its establishment to the end of 1403 showed that the average annual return of the portfolio is 3.2%, the maximum annual return of the portfolio is 5.84%, the minimum risk of the portfolio is 5.38 (out of 10), and the relative return-risk performance of the fund is 1.12. In addition, the results of stress testing on the fund's performance under critical scenarios (economic recession and boom, and inflation) indicate the stability of the proposed model in different economic conditions. Also, in this research, a proposed fuzzy decision-making system including the parameters: fund risk tolerance (RT), maximum fund portfolio return (R), minimum fund portfolio risk (δ), project risk level (PR), and applicant risk level (T) was designed and implemented to help the fund's senior managers make decisions. https://systems.eyc.ac.ir/article_728310.html The present study analyzes and prioritizes the factors affecting the social acceptance of green energies in Iran. Given the importance of renewable energies in reducing environmental pollution and the limitations of fossil resources, examining the factors influencing the acceptance of these energies has gained more importance. The research was conducted using a combination of qualitative and quantitative methods in two stages. In the first stage, a comprehensive review of scientific resources was conducted, examining 129 articles from reputable databases, and eventually, 48 relevant articles were selected, identifying 15 types of nudges. In the second stage, using the fuzzy Delphi method and collaboration with 17 experts in the fields of energy, behavioral economics, social psychology, energy management, and social marketing, the identified nudges were prioritized based on three criteria: impact, feasibility, and social acceptance in Iran. The results showed that informational nudges, green default options, visual labeling, energy consumption feedback, social norms, and well-designed financial incentives received the highest ranks. Additionally, experts introduced four new nudges including indigenous-cultural nudges, religious-ethical nudges, family educational nudges, and influencer-based nudges. These findings can provide guidance for policymakers and planners to promote renewable energies and create a suitable platform for the social acceptance of these energies in Iranian society. https://systems.eyc.ac.ir/article_728820.html This study presents an integrated quantitative model for optimizing sub-hub location selection in Iran's air transport industry by combining technical and marketing criteria. Given existing challenges such as demand dispersion, fleet limitations, and sanctions, the research employs a hybrid methodology incorporating spatial analysis, mathematical modeling (the dynamic p-median problem), and metaheuristic algorithms (genetic and tabu search). The data includes actual statistics from Iran's domestic flights in 2021, an aerial distance matrix among 20 selected airports, and marketing indicators (customer satisfaction, advertising, seat occupancy rate) collected from airport and airline statistical yearbooks. Findings indicate that selecting four sub-hubs (Tehran, Bandar Abbas, Tabriz, Sari) while considering technical criteria (aerial distance, airport elevation, fleet capacity) and marketing factors (demand elasticity, advertising costs) can achieve 87% demand coverage with a 25% reduction in operational costs. Additionally, integrating dynamic pricing strategies with hub-and-spoke network design led to an 18% increase in seat occupancy rates. The results provide a novel decision-making framework for the aviation industry in developing countries facing similar conditions. https://systems.eyc.ac.ir/article_728433.html In recent decades, technological advancements have presented both opportunities and challenges for the banking sector. Among these, forecasting emerging technologies has become a strategic capability for guiding technology-related decision-making. However, many Iranian banks lack structured systems for monitoring and analyzing future technological developments. This study aims to develop a conceptual framework for forecasting emerging technologies in Iran’s banking industry using a mixed-methods approach. In the qualitative phase, thematic analysis of semi-structured interviews with 10 banking experts led to the identification of four main categories of influencing factors: organizational, technological, environmental, and cultural. In the quantitative phase, the Fuzzy Delphi Technique and the Fuzzy Analytic Hierarchy Process (FAHP) were employed to determine the relative weight of each factor and its sub-indicators. The results indicate that organizational and technological factors are the most critical for successful technology forecasting. The final outcome is a four-layered model that can be used to design a structured forecasting system within Iranian banks. This model serves as a practical tool for banking managers and policymakers to prioritize digital transformation initiatives, develop technology roadmaps, and enhance foresight capabilities. Additionally, by integrating thematic analysis and FAHP, this research contributes to the theoretical development of localized foresight models in the financial services industry, offering a robust framework for strategic planning in technology-driven environments. https://systems.eyc.ac.ir/article_728863.html This fully connected neural network-based control chart is developed for monitoring the dispersion of multivariate processes with two important features: (1) the ability to be used for monitoring the dispersion of processes with high-dimensional data streams, and (2) the absence of the need to establish restrictive statistical assumptions, such as normality of the quality characteristics under study and the independence of observations in the samples taken. An important challenge that we usually face in training neural networks is overfitting or generalization failure. In this study, two tools of dropout layer and weight regularization are used in network design to face the mentioned challenge. In addition, in order to better train the neural network and unlike most control charts based on learning tools that use a two-class pattern of zero and one as target values, in this study the target values are determined based on the size and number of shifted components, so that as the shift size and the number of shifted components increase, the target values also increase. Next, in order to increase the power of the developed control chart in detecting disturbances in the covariance matrix elements, an improved version is presented with the help of two sensitizing rules 2 out of 3 and 4 out of 5. The performance of the proposed approaches is compared with two control charts ATL and RPLR using a numerical example. The results show that the approach equipped with sensitizing rules performs better than the competing charts in terms of two indices ARL and SDRL. https://systems.eyc.ac.ir/article_728521.html In recent years, advances in technology, increased business complexity, and crises such as COVID-19 have highlighted the need to rethink supply chain management. The resilient supply chain approach—emphasizing resilience, sustainability, agility, and digitalization—offers a modern pathway to long-term organizational efficiency. This study evaluates and selects suppliers for a closed-loop supply chain under uncertainty. Key criteria were identified through literature review and expert consultation, then weighted using the fuzzy–stochastic Best–Worst Method (BWM). Suppliers were subsequently assessed and ranked via the fuzzy–stochastic TOPSIS method. Findings reveal that beyond traditional factors such as cost and quality, aspects like backup supplier availability, waste management, and fair labor compliance are critical. A medical equipment industry case study validated the model’s effectiveness in identifying top suppliers and enhancing supply chain performance, underscoring the importance of sustainability-focused, long-term strategies over purely economic ones. The study’s novelty lies in a two-stage decision-making framework that integrates resilient supply chain principles, closed-loop structures, and dual uncertainties (fuzzy and stochastic), offering a robust tool for managing complex, ambiguous procurement environments. https://systems.eyc.ac.ir/article_728901.html This research aims to design and elucidate a data governance model in smart government organizations, with an emphasis on information and media management within the Industrial Development and Renovation Organization of Iran (IDRO). In the digital age, data is recognized as a strategic asset for government organizations, necessitating proper governance for maximum utilization and mitigation of associated risks. The current study employs a mixed-methods approach (qualitative-quantitative), utilizing grounded theory, expert interviews, document analysis, and surveys. In the qualitative phase, semi-structured interviews were conducted with 25 specialists and senior managers from IDRO. In the quantitative phase, a researcher-made questionnaire was distributed among 162 experts and middle managers of IDRO. The research findings led to the identification of 6 main dimensions, 15 components, and 57 indicators for the data governance model in IDRO. The identified dimensions include: Structure and Organization, Processes and Methods, Technology and Infrastructure, Human Resources, Organizational Culture, and Information and Media Management. The findings revealed a significant relationship between information and media management and other dimensions of data governance, with the information and media management dimension playing a crucial role in the success of IDRO’s data governance model, exhibiting a direct effect coefficient of 0.78. Finally, a paradigmatic data governance model for IDRO is presented, along with practical strategies for its implementation. https://systems.eyc.ac.ir/article_733640.html Wheat is a key element of national food security, providing a major share of the calories and protein required by the population and therefore playing an important role in public health and livelihoods. Ensuring its consistent production and reliable access is essential for economic stability, food equity, and social resilience. In this regard, this study develops a multi-period, multi-product stochastic optimization model to design a resilient wheat supply chain under disruption risks. The supply chain includes suppliers of agricultural inputs (seeds, pesticides, and fertilizers), farms, wheat suppliers, storage silos, processing factories, livestock feed customers, cosmetics and hygiene industries, and flour consumers. To address weather-related disruptions affecting farm output, the model incorporates additional wheat procurement from supplier - considering quantity discounts - as a resilience strategy. Circular economy principles are also considered through selling straw, bran, and damaged grains to cosmetics and hygiene, and livestock feed industries. The objective of the model is to maximize the total profit of the supply chain by determining product flows between different stages, flour production quantities, inventory levels of agricultural inputs, wheat, and flour, supplier selection, and the location and capacity of silos. The model is then applied to a numerical example, and the computational results are presented. https://systems.eyc.ac.ir/article_729172.html In modern manufacturing systems, preventive maintenance is recognized as a key strategy for optimizing performance and reducing costs. This research aims to develop a novel preventive maintenance model for high-speed press machines. The proposed model, considering operational constraints and targeting the minimization of time and cost while maximizing reliability, is formulated as a multi-objective optimization problem. To solve this problem, the metaheuristic algorithms NSGA-II and Grey Wolf Optimizer have been employed, and the model’s effectiveness has been evaluated through solving small-scale examples. The results indicate that the proposed model can significantly reduce maintenance costs and simultaneously minimize machine downtime by determining optimal times for preventive maintenance activities. Furthermore, the suggested preventive maintenance model improves system reliability, leading to increased productivity and reduced costs associated with sudden failures. This model can serve as an effective decision-making tool for managers and engineers in various industries. https://systems.eyc.ac.ir/article_729268.html In infrastructure projects, delay factors are often interlinked through complex causal relationships, making the accurate identification of the most critical causes dependent on comprehensive analytical methods. Fuzzy Cognitive Maps (FCMs) are widely regarded as effective tools for modeling such complexities. However, most existing studies rely on simple or weighted averaging to aggregate expert opinions, often neglecting the cognitive and relational dynamics between experts. This oversight can undermine the validity of results and ignore minority perspectives. This study proposes a novel approach that simultaneously incorporates both task conflict (differences in expert assessments) and relational conflict (variations in mutual trust) into the aggregation process. The method was applied to evaluate delay factors in a 230 kV transmission line project located in the Deylam Special Economic Zone. The findings revealed that using simple averaging resulted in final node values falling within a narrow range (0.77–0.82), offering limited differentiation in factor prioritization. In contrast, the conflict-resolution-based approach significantly expanded this range (0.64–0.95) and altered the distribution of factor importance. For instance, the weight of “lack of coordination among local entities” increased from 0.77 to 0.95, while the influence of “sudden increase in metal prices” dropped from 0.82 to 0.64. These results underscore the value of explicitly addressing conflicts during expert aggregation, demonstrating how such structured consideration can improve the validity and explanatory power of FCM outcomes—ultimately providing a more robust tool for decision-making in complex project environments. https://systems.eyc.ac.ir/article_729573.html Given the crucial role of project managers in advancing sustainability goals, identifying and developing their social competencies is a key step toward achieving sustainable project management. This study aims to develop a model of social competencies aligned with sustainable project management in the construction industry. A mixed-methods approach was adopted. In the qualitative phase, using grounded theory methodology and conducting 10 semi-structured interviews with experts, the initial set of social competencies was identified and categorized into five groups. Among them, the "strategic competencies" group was selected as the core category representing the key social competencies for sustainable management. In the quantitative phase, the fuzzy SWARA multi-criteria decision-making method was used to prioritize these competencies. The questionnaire was administered in two stages: first with 50 specialists for ranking, and then with 15 experts for determining relative importance. The results revealed that “effective stakeholder engagement,” “strategic and long-term thinking,” and “negotiation and persuasion skills” were the top priorities among key social competencies. Furthermore, the conceptual alignment between these competencies and international frameworks—such as the IPMA Individual Competence Baseline Version 4.0 and Boyatzis's competency model—indicates that the study’s findings are in line with established global standards. The findings also highlight that a major barrier to achieving social sustainability in projects is the short-term economic mindset of managers and their lack of awareness of sustainability concepts. Accordingly, the identified competencies can serve as effective tools for assessing and enhancing the social competencies of project managers toward sustainable project management. https://systems.eyc.ac.ir/article_729628.html The main objective of the present study is to evaluate the performance of petrochemical companies producing methanol using data envelopment analysis based on the simultaneous use of two natural and managerial principles. Because, in DEA, natural accessibility shows the potential capacity for improvement based on the mathematical frontier of efficiency, while managerial accessibility focuses on the possibility of actually realizing these improvements. The combination of these two perspectives reveals the gap between theoretical and practical efficiency and provides a basis for designing realistic performance improvement programs. For this purpose, a statistical population consisting of four petrochemical companies producing methanol active in the country has been selected. The research period for data analysis is related to the year 1401. The results obtained showed that the best and highest efficiency is related to Fanavaran, Zagros and Khark companies with a relative efficiency of 100 percent, and the Marjan production unit has been determined as an inefficient unit. In addition, it has been prioritized using the Anderson-Peterson method. Based on the priority obtained, Khark Company ranked first, Zagros Company ranked second, Fanavaran Company ranked third, and Marjan Company ranked fourth. Also, a comparison based on efficiency scores with basic models including BCC and CCR is presented to examine the capability of the proposed model. The difference in inefficiency between the proposed method and the BCC method is minor and is at the level of 0.012. Finally, the changes in the amount of manageable input have been calculated, in which case the maximum amount of reduction in the number of manageable inputs is calculated to be 0.141 units. The results of this study allow managers of petrochemical companies producing methanol to identify inefficient units with potential for improvement and direct organizational resources to them in a targeted manner. Also, by combining natural and managerial perspectives, managers can formulate realistic operational plans to improve productivity, optimize production capacity, and manage assets, and implement corrective actions with specific prioritization and timing. https://systems.eyc.ac.ir/article_729662.html The increasing complexities and organizational changes in the upcoming era inevitably direct researchers towards deeper contemplation of systemic methodologies and their application to address emerging issues. This article employs Soft Systems Methodology (SSM) to enhance System Dynamics (SD) in solving complex organizational issues, aiming to further leverage the capabilities of this combined approach in scenario planning and futures studies, particularly in the strategic domain. To evaluate the proposed combined methodology in practice, it was applied to better implement and prioritize strategies at the Statistical Center of Iran as a case study. Initially, the conceptual model of the employee motivation system (identified as the priority strategy) was outlined by various stakeholders using SSM. Subsequently, a causal loop diagram was extracted, and after modeling, testing, and implementing the model under different scenarios, the prioritization of operational strategies to improve the employee motivation system was conducted. Thus, by simultaneously combining both methods, SSM’s ability to engage stakeholders with diverse worldviews was harnessed to create a broader picture of strategies and to cultivate a strategic culture, while SD’s strengths in scenario planning were utilized for better implementation and prioritization of strategies in the relevant organization. This research aims to clarify the steps of the combined methodology in a way that can be applicable and beneficial for strategy development in other organizations, especially public organizations. https://systems.eyc.ac.ir/article_729829.html In recent years, Iran has faced increasing challenges in meeting its energy demands, as evidenced by frequent power outages in both residential and industrial sectors. This situation underscores the urgent need for sustainable and reliable energy solutions. This study proposes a hybrid multi-criteria decision-making framework to identify and rank the most suitable regions for developing solar power plants across the country. The study employs a fuzzy logic-based approach to integrate multiple methods while accounting for the inherent uncertainty in data and expert opinions. In the first stage, Data Envelopment Analysis (DEA) was utilized to evaluate the efficiency of all provinces based on climatic and geographical indicators, including solar radiation, sunshine hours, precipitation, cloud cover, and altitude, using data from the Iran Meteorological Organization. This process facilitated the screening and identification of provinces more suitable for establishing solar power plants. Subsequently, key and influential criteria for optimal site selection were determined and weighted using the Fuzzy Analytic Hierarchy Process (FAHP) based on expert judgments. Finally, by integrating the results of the previous stages, the selected provinces were ranked using the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). The results indicate that the central, southern, and southeastern provinces of Iran possess the highest potential for solar energy development. The findings of this research can assist decision-makers, planners, and investors in the energy sector to take a significant step toward advancing Iran’s transition to a more sustainable future and reducing its dependency on oil and gas. https://systems.eyc.ac.ir/article_729999.html The rapid recognition and acceptance of the sharing economy in the contemporary world, stemming from the development of smart platforms across various industries aimed at improving the quality of life for people worldwide, has driven businesses toward developing and utilizing sharing business models. The present research seeks to provide a new, practical, and simple method to assist businesses in selecting a sharing business model. This research is applied in terms of objective, quantitative in terms of method, and pragmatic in terms of research philosophy. To this end, after conducting library studies and reviewing multi-criteria decision-making methods and previous business model selection approaches, the identification of indicators for selecting a business model prior to implementation—as the indicators of the proposed method—and subsequently the development of a novel method for selection based on multi-criteria decision-making approaches and researchers' innovations tailored to the conditions of the business model selection problem, have been addressed. The findings demonstrate the development of a multi-criteria method for selecting a sharing business model prior to implementation, based on 8 indicators: value creation, complete value proposition, appropriate market size, access to potential customers, willingness to exert effort, cost-effective expenses, superiority over competitors, and existence of entry barriers, along with a novel scoring multi-criteria decision-making method known as evaluation based on distance from the ideal solution, developed by the researchers. Finally, the proposed method has been applied for evaluating and selecting a sharing business model in an Iranian sharing business active in one of the innovation centers for this purpose. https://systems.eyc.ac.ir/article_730423.html Knowledge-based policymaking is an emerging approach in modern governance that emphasizes the use of scientific evidence, empirical data, and research to inform public decision-making. This approach has considerable potential to enhance the effectiveness and legitimacy of policies. However, barriers such as limited access to data, difficulties in interpreting and translating knowledge, and the gap between research and practice remain major challenges. This study conducts a bibliometric analysis of the scientific literature on knowledge-based policymaking to identify key trends, research gaps, and future directions. Data were collected from the Web of Science and Scopus databases, covering 317 publications published between 1997 and 2025. The analysis involved performance evaluation, citation and co-citation analysis, scientific collaboration assessment, and co-word mapping to explore intellectual structures and thematic developments. The findings reveal that this field is growing at an annual rate of 6.61%, with countries such as the United Kingdom, Australia, Canada, the United States, and the Netherlands playing leading roles. Influential journals, authors, and works were identified, and the intellectual foundations of the field were mapped. Emerging trends include the use of big data and artificial intelligence in policymaking, along with the growing role of public participation. Overall, this study provides a comprehensive overview of the field, emphasizing the need to improve data accessibility, strengthen the link between research and policy, and expand international collaboration. The results offer valuable insights for researchers and policymakers and serve as a solid bibliometric reference for future research. https://systems.eyc.ac.ir/article_730832.html In confronting the complex challenges of the biopharmaceutical industry, such as demand fluctuations, sensitive biological processes, stringent quality requirements, and sustainability objectives, the development of intelligent data-driven models for production planning is deemed essential. The present study introduces a comprehensive hybrid model aimed at simultaneously optimizing economic profitability and environmental sustainability. To this end, pharmaceutical demand was forecasted using Long Short-Term Memory (LSTM) neural networks—a model capable of learning intricate temporal dependencies. The predictive accuracy of the model was evaluated for 9 selected drugs across three demand scenarios (low, medium, high), with results demonstrating high alignment with actual values and robust performance stability. Subsequently, a multi-objective mixed-integer linear programming (MILP) model incorporating conflicting economic-environmental objectives was designed and solved utilizing Genetic Algorithm (GA) and the ε-Constraint method. The proposed model was validated using real data from Iranian pharmaceutical companies, and the findings underscore the superior capability of the suggested approach in achieving a balance between profit and pollution, resource management, and optimal decision-making under uncertainty conditions. https://systems.eyc.ac.ir/article_730974.html One of the crucial factors influencing economic growth and sustainable development is the improvement of productivity in production factors. In today’s world, scarcity of resources and the necessity of environmental preservation have led productivity approaches toward green productivity and the economy toward a circular model. Achieving circular economy goals requires appropriate methods and tools capable of evaluating the environmental and financial impacts of process improvements. Material Flow Cost Accounting (MFCA), as a standardized tool for assessing material and financial flows within companies, provides an opportunity to meet both economic and environmental objectives simultaneously. In this study, MFCA was implemented in the brine purification unit of a chlor-alkali complex to optimize its financial productivity system while integrating circular economy principles into the process. By analyzing material flows and associated costs, hidden losses related to negative by-products such as sludge and scale were identified, and improvement opportunities were highlighted. A comprehensive analysis before and after implementing MFCA-based improvements revealed a reduction of 121 tons in undesirable products and a 0.795% financial saving in waste costs. Consequently, the company’s profit increased by approximately 11 billion IRR within six months (2022) due to cost reductions. These findings not only demonstrate the effectiveness of MFCA in waste minimization and system productivity enhancement but also emphasize its positive economic impact. The promising results of this research provide a practical framework for extending MFCA application across other industrial sectors, enabling data-driven decision-making for managers. https://systems.eyc.ac.ir/article_731287.html In today’s increasingly competitive environment, achieving innovative work behavior has become a strategic imperative. This research focuses on identifying the key factors influencing this behavior within the Iranian industrial sector. The main objective of the study was to investigate the impact of responsible leadership and person-organization fit on employee innovative work behavior, considering the mediating role of knowledge sharing. The statistical population of the study included all employees of HAPCO Arak Company (1800 people in 2024), from whom a sample of 200 individuals was selected using random sampling. Data were collected using standard questionnaires and analyzed using Structural Equation Modeling with SPSS and PLS software. The reliability and validity of the constructs were confirmed using Cronbach’s Alpha and convergent and discriminant indices. The results showed that Person-Organization Fit, with a beta coefficient of 0.49, has the strongest positive direct impact on innovative work behavior. Furthermore, the mediation of knowledge sharing was confirmed in both pathways, and the stronger path, according to the Sobel test statistic, related to Person-Organization Fit and innovative work behavior. By evaluating the relative intensity of the impact of organizational and individual factors on innovation as the main driver, this research provides an operational framework for HAPCO managers to directly guide their recruitment and development strategies toward innovative behaviors. https://systems.eyc.ac.ir/article_731616.html This study aimed to identify and prioritize maintenance strategies for production systems based on sustainability principles in the automotive parts industry. The present study is applied in terms of purpose and descriptive-survey in terms of data collection. Using literature review and expert opinions, sustainability criteria were identified and confirmed via the Delphi method within four dimensions: economic, social, environmental, and technical. The weights of the main criteria and sub-criteria were then calculated using the FUCOM multi-criteria decision-making method. Finally, seven maintenance strategies were ranked using the SAW and TOPSIS techniques. The statistical population consisted of four automotive parts manufacturing companies, and data were collected through questionnaires and surveys from 10 experts. The findings revealed that among the sustainability dimensions, the social dimension held the highest priority, followed by the economic, technical, and environmental dimensions. Among the sub-criteria, "Decommissioning worn-out equipment and replacing it with green equipment" ranked first, followed by "Compliance with safety and environmental standards" and "Employee expertise." Regarding the maintenance strategies, the Preventive Maintenance (PM) strategy ranked first, the Total Productive Maintenance (TPM) strategy ranked second, and the Predictive Maintenance (PreM) strategy ranked third. Given the high priority of the social and economic dimensions, it is recommended that managers in the automotive parts industry focus on preventive and total productive maintenance strategies to enhance both productivity and the sustainability of their operations. https://systems.eyc.ac.ir/article_731649.html The resource-constrained project scheduling problem seeks to find an appropriate sequence for performing the activities of a project such that precedence constraints in the project network and various types of resource limitations are satisfied simultaneously, while specific performance criteria—such as project completion time, activity execution costs, the number of delayed activities, and so on—are optimized. With the development of this problem in recent years, an activity can be performed in several possible modes (for example, each activity can be executed within a specific duration and with a certain amount of renewable or non-renewable resources). This new concept has led to the development of one of the most general forms of scheduling problems, known as the multi-mode resource-constrained project scheduling problem. On the other hand, as humanity steps into the third millennium, it faces numerous environmental and ecological issues every day. The effects of environmental pollution and the destruction of natural resources threaten natural life more than ever. Therefore, preventing the destructive impact of unbalanced economic and industrial activities on the environment, as well as restoring and repairing it, have become major concerns in modern life. Given the current importance of green criteria, the aim of this paper is to mathematically model and optimize the multi-mode resource-constrained project scheduling problem while considering gas emissions. After modeling the problem as a mixed-integer programming formulation, the results were analyzed and evaluated. https://systems.eyc.ac.ir/article_731724.html Total Quality Management (TQM) is a critical management philosophy for achieving sustainable competitive advantage. Therefore, identifying and prioritizing its key success factors (CSFs) in capital-intensive industries such as petrochemicals is of great importance. The present study aimed to identify and rank the key factors affecting the successful adoption of TQM in Shiraz Petrochemical Company. This study uses a combined Delphi approach and multi-criteria decision-making in a fuzzy environment. In the first step, by extensively reviewing the literature and using the fuzzy Delphi technique, 14 key factors in four main dimensions were identified and finalized. In the second step, the Sequential Priority Based on Importance Assessment (SWARA) method was used to weight and final rank these factors. The results of the analysis showed that the factors related to the human resources dimension are the main axis of TQM success in the studied organization. The factor “Employee participation and empowerment” was recognized as the most critical factor by gaining the highest weight. After that, the factors of "Employee Evaluation" and "Education and Training" ranked second and third. The findings strongly indicate that in the specific context of the petrochemical industry, investing in the soft and human dimensions of the organization has the greatest impact on the success of TQM implementation. This research provides a practical road-map for managers of Shiraz Petrochemical Company to optimally allocate resources and focus on developing a culture of participation, reviewing evaluation systems, and strengthening training programs. https://systems.eyc.ac.ir/article_732035.html This study examines multi-objective modeling for cost management in the optimal allocation of relief goods in crisis situations, focusing on the importance of time and resources in emergencies. The main goal is to develop a mathematical model for optimization that enables more effective and efficient allocation of relief goods by incorporating uncertainty in decision-making, reducing costs, and improving crisis response. The article presents a multi-objective, multi-period mathematical programming model for fair distribution of relief items and develops a multi-objective, multi-level humanitarian supply chain for equitable distribution of livelihood packages to counter crises. Additionally, a metaheuristic method is developed to solve the model for large-scale problems, and Pareto solutions are evaluated. The research examines four dimensions of humanitarian logistics indicators: access and transportation costs, unmet demand rate per period, the distance between the demand fill rate and the ideal satisfaction rate over the entire period, and environmental risks. The model is designed for allocating essential materials (water, food, medicine, equipment, clothing, and blankets) from multiple relief centers to various affected areas to ensure fair behavior and better alignment with real conditions. Results show that increasing epsilon up to a certain value causes negligible changes in objective function values, but beyond that, it leads to significant changes; the feasible region and improvement vector for objective functions are determined for epsilon values between 50 and 900, with optimal epsilon values for the first to fourth objective functions being 500, 150, 600, and 150 and 600, respectively. The proposed model is suitable for sudden large-scale local natural disasters (not national) occurring in urban areas with a certain resident population and cannot be directly used for storms or other low-impact, dispersed disasters. Ultimately, this research helps improve relief processes, reduce associated costs, and leads to saving human lives and mitigating crisis damages. https://systems.eyc.ac.ir/article_732176.html In the country's five-year development plans, it is emphasized that one-third of economic growth should come from productivity. The family, as a unit of production and consumption of resources, plays an effective role in managing the flow of productivity. In the present study, first, in order to formulate a national macro-map and institutional division of labor, governance goals were set in order to achieve family economic productivity (governance issues system), and then, by applying the sustainable systems model and adapting it to the functional reference model prepared, the actors and role-makers of the family economic productivity issue in sustainable subsystems were analyzed. With library studies and interviews with experts, the institutional mapping approach was applied by using task-function and institution-function matrices to separate governance goals. Also, the role-making of actual actors based on the proposed functional framework in achieving each governance goal was analyzed. The importance of sustainable modeling, in terms of macro-architecture based on a comprehensive and systemic view, is the analysis and correct placement of actors in order to ensure the stability and survival of the system. The results show that in all goals and issues of the family economy from a governance perspective, either the actual actors have a weak role to play or there is no specific custodian to solve the problem and a severe vacuum is felt in the role-players. The general approach in the desired situation is to reduce the managerial role of government institutions and move towards empowering and supporting the family. https://systems.eyc.ac.ir/article_732216.html With the rapid rise in the use of glass containers in the food and pharmaceutical industries, demand for a sustainable and efficient packaging supply has increased significantly. At the same time, environmental concerns and regulatory requirements regarding waste recycling have turned the design of glass-container supply chains into a strategic challenge in industrial management. In response, this study proposes a three-phase hybrid approach for designing a closed-loop supply chain for glass containers under transportation-infrastructure disruption risks and uncertainty. In Phase 1, a system dynamics approach is employed to forecast customer demand. In Phase 2, suppliers are ranked using the Combined Compromise Solution (COCOSO) multi-criteria decision-making method. Building upon the outcomes of these phases, Phase 3 develops a multi-product, multi-period stochastic programming model that minimizes the total cost of the glass-container supply chain while determining decisions such as supplier selection, production quantities, transportation flows across echelons, lateral transshipments among distribution centers, recycling, disposal, and shortage management. The proposed model is implemented on three numerical examples, and the results of the sensitivity analysis on the key parameters of the problem are presented. https://systems.eyc.ac.ir/article_732290.html The construction industry in Iran faces challenges such as delays, cost increases, and quality decline, a significant portion of which stems from weaknesses in communication management among project stakeholders. The aim of this research is to identify key factors affecting communication weaknesses and to present a hybrid model for analyzing and predicting their impact on the performance of construction projects. The present study employs a mixed exploratory-explanatory approach. In the qualitative phase, indicators related to communication weaknesses were extracted through a systematic review of sources and content analysis of documents. In the quantitative phase, data from 114 valid questionnaires from employees of construction projects in Zanjan city were analyzed using Structural Equation Modeling (SEM) and Random Forest (RF) algorithm. The SEM model results showed that information flow, project management, and alignment of human resources with organizational strategy have the greatest direct impact on project performance. The machine learning model also identified these three factors as the strongest predictive variables for communication weaknesses and achieved a prediction accuracy of 0.87 and an AUC value of 0.93. The combination of the two methods provided the possibility of simultaneous analysis of theoretical relationships and empirical prediction. The findings indicate that strengthening the project information management system, clarifying the managerial structure, and aligning human resources with organizational goals can significantly reduce the risk of poor communications. The main innovation of the research is the application of the hybrid SEM-ML approach in the context of national projects in Iran and presenting a model that, in addition to analyzing causal relationships, has the ability to rank and data-driven predict key factors. https://systems.eyc.ac.ir/article_732324.html In recent years, statistical time series models—particularly the ARIMA model—have been widely used as effective tools for stock price forecasting. Despite its acceptable accuracy, the model’s sensitivity to the number of historical data points remains a major limitation in practical applications. This study aims to enhance prediction accuracy and stability by proposing a new model called RARIMA (Reset Auto Regressive Integrated Moving Average), developed based on the ARIMA framework. The proposed model employs a Fast-Learning Reset approach to reduce the effect of the selected historical data length. Stock price data from 18 companies across four industries—steel, petrochemical, banking, and automotive—were analyzed over the period April 2018 to January 2024. To evaluate model performance, the Mean Relative Error (MRE) and the TOPSIS ranking method were used to compare short-term and long-term forecasting accuracy. The results indicate that the RARIMA model improves prediction accuracy by up to 75% compared to the traditional ARIMA model, while significantly reducing its sensitivity to the size of historical data. Accordingly, the proposed model can serve as a reliable and efficient tool for financial analysts and decision-makers in the capital market. https://systems.eyc.ac.ir/article_732506.html Systems thinking serves as a foundation that, through its unique capabilities, facilitates the understanding of complex issues and provides opportunities for making optimal choices for a better life. It is fitting that learning it begins from childhood in primary school. The objective of this research was to design a model for cultivating systems thinking in primary school students, conducted using the qualitative method of grounded theory. The statistical population consisted of experts in the fields of educational sciences, counseling, and psychology familiar with the primary education domain. From among them, 25 individuals were selected through theoretical sampling for in-depth interviews, and the interviews continued until theoretical saturation was achieved. The validity of the findings was assessed using member checking and pilot interviews. For the analysis of qualitative data, open, axial, and selective coding were employed. The results indicate that 1,390 initial conceptual statements, comprising 23 main categories and 90 subcategories, were identified within the six dimensions of the conceptual model, including: causal conditions (school, family, curriculum), the core phenomenon (cultivating systems thinking), contextual conditions (cognition, holistic view, managerial competencies, adequate resources and facilities, flexibility in the educational system, alignment of social institutions with the school), intervening conditions (social environment, political culture, economic system, cultural conditions), strategies (utilizing a participatory system, fostering life skills, teaching, motivational strategies), and consequences (comprehensive perspective, skill enhancement, academic success, individual development, community development). The integration of categories based on existing relationships forms the conceptual model that reflects the pattern for cultivating systems thinking in primary school students. https://systems.eyc.ac.ir/article_732956.html This study systematically identifies and organizes the antecedents, consequences, and methods for improving the productivity of supervisors in gas distribution projects at the Khorasan Razavi Gas Company. Employing an interpretivist-constructivist approach, the research integrates two methodologies: grounded theory and TRIZ (specifically, Alʹtshuller’s Contradiction Matrix and the 40 inventive principles). Data were collected through semi-structured, event-based interviews with 19 supervisors. Using constructivist grounded theory within MAXQDA software, the analysis identified five primary categories influencing supervisor productivity: (1) training and empowerment, (2) motivation and job satisfaction, (3) effective organizational communication, (4) work-life balance, and (5) psychological well-being. Initiatives to enhance these factors, however, often entail significant costs, time, and energy. To resolve these inherent contradictions, the study applied the TRIZ Ideal Final Result concept and Contradiction Matrix via CREAX software, deriving nine suitable inventive principles: Merging, Prior Action, Continuity of Useful Action, Blessing in Disguise, Skipping, Feedback, Mechanical Substitution, Fluid System, and Changing Properties. The strategic application of these principles demonstrates that it is possible to enhance core productivity while simultaneously mitigating the associated resource constraints. https://systems.eyc.ac.ir/article_733212.html Unplanned power outages disrupt grid stability and increase operational costs, posing a major threat to power system efficiency. In this paper, we propose a robust outage prediction model that combines an Autoencoder-based feature extractor with a residual multi-layer perceptron (MLP) classifier. The novelty of our approach lies in its ability to maintain high predictive performance while eliminating reliance on geographic features such as latitude and longitude—commonly required by traditional models. We first train the Autoencoder on a rich, unlabeled dataset of weather and energy demand data collected over two decades (2000–2024) across Maryland, USA. The learned latent representations are then used to augment a supervised classification model trained on labeled outage data. Our final model achieves an F1-score of 81% even without location-based features, compared to 90% when using all features. This generalizability enables the deployment of predictive tools in previously unseen regions, directly enhancing grid flexibility, reliability, and system efficiency. https://systems.eyc.ac.ir/article_733316.html The objective of this research is to design a model of the requirements for the sharing economy in production systems within the textile industry in southeastern Iraq—a sector that, despite its potential capacities, lacks a local framework for identifying the requirements and causal relationships of this approach. To this end, a systematic literature review was first conducted to identify the requirements. Then, the extracted indicators were prioritized using the SWARA method with the assistance of experts. Finally, by utilizing fuzzy cognitive mapping and FCMapper software, the causal relationships between factors and the influential and influenced scenarios were explained. The findings indicate that four factors—"senior management support," "supportive and regulatory policies," "stakeholder awareness," and "culture of adopting the sharing economy"—play the most significant role in the successful implementation of the sharing economy. Additionally, "identifying excess capacities and underutilized resources" as a foundational factor has the greatest impact on other requirements. Therefore, to establish the sharing economy in the textile industry in southeastern Iraq, excess capacities must first be systematically identified and activated. The proposed model can serve as a practical guide for policymakers and managers in applying the sharing economy in manufacturing industries. https://systems.eyc.ac.ir/article_733533.html One of the persistent challenges in industry is achieving a balance between uninterrupted production and minimizing inventory-holding costs—an objective that traditional inventory management methods often fail to meet due to their weaknesses in forecasting irregular demand and overlooking operational constraints. In this study, we attempt to provide an integrated solution to this problem by combining accurate demand forecasting with mathematical modeling. A key strength of the proposed model is its flexibility in selecting the procurement method; that is, the system can choose the optimal option between regular purchasing and emergency purchasing based on prevailing conditions. In designing the model, real-world constraints—such as budget limits (for both critical and non-critical items) and warehouse capacity—are precisely incorporated, and the safety stock level dynamically adjusts according to demand fluctuations. The results obtained from implementing the model on data from 60 items indicate that moving away from one-dimensional policies and adopting a hybrid strategy leads to cost reduction and improved system stability, even under financial and space limitations. https://systems.eyc.ac.ir/article_733605.html Effective risk management is critically important for Engineering, Procurement, and Construction (EPC) projects in the petrochemical industry, given their complex, capital-intensive, and high-risk nature. Traditional methods like Failure Mode and Effects Analysis (FMEA), despite their widespread use, suffer from limitations such as equal weighting of criteria and an inability to handle large volumes of risks efficiently. To bridge this gap, this study proposes a novel hybrid framework for the systematic identification, assessment, and prioritization of risks. The proposed model integrates the core FMEA approach with the Best-Worst Method (BWM) for determining optimal weights of Severity, Occurrence, and Detection criteria, and the Combined Compromise Solution (CoCoSo) method for precise risk ranking. A key innovation is the incorporation of the K-means clustering algorithm, which uncovers hidden patterns and groups risks with similar profiles, providing managers with a holistic view for developing integrated mitigation strategies. The framework is applied to a real-world case study in Iran's petrochemical industry involving 123 identified risks across eight dimensions. Clustering results reveal five distinct risk clusters, facilitating focused attention on critical areas. Furthermore, the final CoCoSo ranking identifies the most significant risks in domains such as knowledge management from past projects, price volatility of equipment, and integrated information management. This integrated framework serves as a strategic tool, enhancing the capability of EPC project managers in optimal resource allocation and effective response planning for complex project risks. https://systems.eyc.ac.ir/article_734036.html This study introduces an innovative approach to addressing the dual challenges of line balancing and sequencing in mixed-model assembly lines through the integration of artificial intelligence. By combining deep neural networks (DNNs) and reinforcement learning (RL), the proposed framework simultaneously optimizes workstation load distribution and task ordering. Historical production data—including task processing times, the total number of workstations, and inter-task dependencies—were collected and structured into predictive models. A Deep Q-Network (DQN)-based RL agent was developed to dynamically assign tasks to stations and determine their execution sequence in real time, aiming to minimize makespan and maximize overall line efficiency. In parallel, DNNs were employed to forecast task processing durations and evaluate the feasibility of relocating tasks across stations. Numerical experiments using real-world production data demonstrate that the proposed method significantly reduces idle time, decreases task waiting periods, and streamlines workflow continuity. Furthermore, benchmarking against conventional optimization techniques—such as Genetic Algorithms and Simulated Annealing—highlights the advantages of this machine learning–driven strategy, particularly in achieving near-optimal solutions more rapidly and with greater adaptability. https://systems.eyc.ac.ir/article_734067.html This study introduces a data‑driven surrogate model based on the Group Method of Data Handling (GMDH) approach, which utilizes artificial neural networks to replace computationally intensive engineering methods such as Finite Element Analysis (FEA), thereby enhancing simulation system efficiency. The approach was developed by focusing on the nonlinear relationships between dimensionless input parameters—including system characteristics (geometry and material properties), operational conditions (impact energy and strain rate)—and the primary output, namely the permanent deformation‑to‑thickness ratio of the sheet. An experimental dataset comprising 65 data points was compiled from actual tests employing hydrodynamic processes to apply loading. The GMDH network, with a 12‑layer architecture and 120 parameters, was trained after data standardization and splitting into training (67%) and test (33%) sets. Its performance was evaluated using metrics such as RMSE (0.884), MAE (0.711), MAPE (6.673%), R² (0.989), and the Willmott index (0.997), which demonstrate high accuracy, absence of overfitting, and a significant reduction in computational time (to seconds versus hours for FEA), thereby improving efficiency in industrial system management. Intrinsic sensitivity analysis, local sensitivity analysis (based on elasticity and partial derivatives), and uncertainty analysis with a confidence band of 0.2167 ranked the importance of input parameters and highlighted the dominant role of operational factors in system optimization. The research innovations include the integration of laboratory data for holistic real‑system modeling, provision of input‑output mapping, and reduction of simulation costs, making the model suitable for industrial applications in manufacturing process optimization and productivity enhancement in automotive industries, and taking a step toward AI‑driven design in systems engineering. https://systems.eyc.ac.ir/article_734248.html In today's world, with the rapid increase in innovations, environmental complexities, and high levels of uncertainty in organizations and societies, traditional leadership approaches are no longer adequate for contemporary challenges, and new concepts such as "sustainable leadership" are developing. This approach focuses on balancing economic, social, and environmental dimensions, with the goal of enhancing resilience, responsibility, and sustainable development. In this study, the innovative method of polar coordinate distance based on lost opportunities in the field of multi-criteria decision-making has been examined for analyzing and evaluating eighteen of the best commercial companies under uncertainty conditions. The advantages of this technique, while possessing a strong logic of lost opportunities and its validation compared to other techniques, include the ability to easily evaluate and rank options. The results show that Company A2, with the lowest lost opportunity value (DOL = 0.0199), ranks first, and Company A11, with the highest lost opportunity (DOL = 0.0808), ranks last in terms of sustainable leadership. Additionally, criteria such as sustainable profitability, occupational health and safety, and waste management, with the lowest lost opportunities and highest performance, rank at the top for evaluating company managers and their sustainable performance, while criteria like market competitiveness and natural resource conservation require strategic reforms and performance improvements. This technique, by combining lost opportunities and using the polar distance approach, provides a unique perspective in evaluation and ranking; such that a smaller distance indicates fewer lost opportunities. This makes understanding and comparing options easier for decision-makers and managers, leading to more precise and effective decision-making in various fields. https://systems.eyc.ac.ir/article_735208.html This study investigates the impact of enterprise social media use on employee productivity, considering the mediating role of self-efficacy and the moderating role of workplace incivility in Hamkaran System, Shatel, and Fanavaran Sepahan Mahan (knowledge-based company). This study is applied in terms of purpose and is classified as descriptive-correlational research in terms of methodology. The statistical population consisted of 565 employees from the three mentioned companies. The sampling design was implemented as a multi-stage sampling method (purposive – purposive – simple random). To determine the sample size, the sample was initially estimated at 229 using Cochran's formula, ultimately resulting in the collection of 210 valid and analyzable questionnaires (effective response rate of 92%). Data were gathered using standard questionnaires comprising 35 items and were analyzed using structural equation modeling with the partial least squares approach via SmartPLS and SPSS software. The results of hypothesis testing showed that the use of enterprise social media has a positive and significant impact on employee productivity as well as on self-efficacy. Furthermore, self-efficacy has a positive and significant effect on employee productivity. Examining the indirect effect using the bootstrap method revealed that enterprise social media has a positive and significant impact on productivity through the mediating role of self-efficacy, with the type of mediation being partial. According to the findings of this study, workplace incivility does not moderate the relationship between enterprise social media and productivity, and the minuscule effect size confirms the absence of moderation in this relationship. Based on the obtained results, business leaders can enhance self-efficacy and consequently increase employee productivity by implementing and utilizing enterprise social media within their organizations, thereby maintaining their competitive advantage over rivals in today's dynamic and competitive environment. https://systems.eyc.ac.ir/article_735344.html The aim of the present study is to develop a data-driven and predictive model for forecasting the product acceptance rate in a lean manufacturing environment using an artificial neural network. In the first step, potential variables influencing product acceptance were identified through a systematic review of the literature and by eliciting the opinions of experts in lean manufacturing and quality management. The final selection of input variables was then carried out using the fuzzy Delphi method. Subsequently, 4,800 data records extracted from food industry production lines over a two-year period were used as the quantitative data for the study. The predictive model was designed and trained based on a feedforward artificial neural network, and its performance was compared with several benchmark regression models, including linear regression, decision tree, random forest, and support vector machine. The results showed that the artificial neural network model, with a coefficient of determination of 0.88, achieved higher predictive accuracy than the benchmark models and demonstrated a strong capability in modeling nonlinear relationships between quality- and process-related variables and the product acceptance rate. Furthermore, variable importance analysis revealed that raw material quality and the percentage of production waste played the most significant roles in predicting the product acceptance rate. The findings indicate that improving a single factor in isolation does not guarantee an increase in product acceptance, and that sustainable outcomes can only be achieved through the simultaneous management of multiple key variables. https://systems.eyc.ac.ir/article_735684.html In today’s competitive landscape, manufacturing companies face increasing pressure to rapidly meet customer needs while simultaneously reducing costs and shortening product life cycles. As a strategic response to this situation, the concept of Agile Product Family Development has emerged, enabling companies to produce various related products that share common components, in order to capture different market segments more quickly and ahead of competitors. However, the effectiveness of this process is often constrained by a lack of understanding of the causal relationships among its influencing factors. This study seeks to bridge this gap by focusing on the automotive industry, and in particular the Iranian automaker SAIPA. The first objective of the research was to identify the critical success factors of the product family development process, and then to model the causal relationships among these factors in order to develop a roadmap for enhancing agility in product family development in the Iranian automotive sector. The research is applied in nature and employs a descriptive–survey method. Through a review of the literature, 29 key factors in agile product family development were identified and classified into five main groups. Subsequently, the fuzzy concept mapping method was used to model the causal relationships among the identified factors, based on responses collected from questionnaires distributed to 16 managers and expert specialists at SAIPA. The proposed model suggests that by focusing on the causal relationships embedded in the model, managers can respond to customer needs while maintaining high quality, low cost, and short product development cycles.