https://www.jise.ir/ Journal of Industrial and Systems Engineering en daily 1 Mon, 18 Nov 2024 00:00:00 +0330 Mon, 18 Nov 2024 00:00:00 +0330 https://www.jise.ir/article_209777.html In a stock portfolio trading system, there are two main tasks: stock selection and portfolio optimization. To consider market dynamics, such a system needs to have appropriate buy/sell trading signals that can be generated by applying a Convolutional Neural Network (CNN) as a powerful classifier. In a CNN architecture, pooling layers operate to decrease the input features' dimensionality while overcoming the overfitting issue. Although several approaches have been proposed for designing this layer based on different applications, especially in image processing, so far, no attempts have been made to design a pooling layer according to the characteristics of an algorithmic trading system. In this paper, an enhanced CNN has been proposed for stock selection in which a new pooling layer called ranked-based time-adjusted weighted pooling layer (RTAWP) has been suggested, in which activation values are ranked according to the recency of price time series. Our simplified RTAWP dramatically reduces the number of parameters while maintaining competing performance. To deal with the tail risk of the constructed portfolios, we consider Conditional Value at Risk (CVaR) as a coherent risk measure in our portfolio optimization model. To show the applicability of our proposed model, we use volume and open-high-low-close prices of sample stocks from the Tehran Stock Exchange during Jan 2020 and Sep 2023. The results indicate that the proposed model provides better computational and financial results for our sample stocks compared with average pooling and other benchmark models. https://www.jise.ir/article_209778.html Information processing styles play a crucial role in mathematics teachers' problem-solving skills, as they directly impact their ability to teach effectively and address educational challenges. A precise understanding of these styles can enhance teaching methods and improve students' problem-solving skills, necessitating the development of a localized model. This article aims to formulate a model of philosophical mindset and mindfulness and examine their effects on information processing styles in mathematics teachers' problem-solving abilities. Developing this model requires an in-depth analysis to assess how philosophical beliefs, attention quality, and focus relate to problem-solving approaches. The study employs a mixed-methods approach (qualitative and quantitative) to explore, describe, interpret, and explain the research topic. Initially, qualitative interviews were conducted to identify model components. The research population comprises experts from the education departments of Districts 1, 2, and 3 in Tehran, who participated in interviews and completed a structured interpretive questionnaire. Data were analyzed using MAXQDA and ISM software. The results indicate that philosophical mindset, mindfulness, and their components significantly impact mathematics teachers' information processing styles in problem-solving. Based on these findings, practical recommendations are provided. https://www.jise.ir/article_210105.html This study examines the supply chain of Tehran Municipality's fruit and vegetable markets, focusing on reducing costs in direct supply markets. The research aims to develop a model of key factors affecting Tehran Municipality's fruit and vegetable supply chain and evaluate corrective policies using system dynamics modeling." The statistical population comprises professionals from Tehran Municipality's direct supply markets and central market with bachelor's degrees or higher in agriculture, finance, business, and related fields. Expert gardeners were selected from among exemplary gardeners across different geographical locations, holding at least a bachelor's degree. The study analyzed the distribution data of direct supply markets over a 12-month period in 2019." The research employed system dynamics methodology using Vensim software to analyze system behavior, focusing on the complexity of relationships between multiple variables, feedback loops within the system, and the cumulative or flow nature of variables. Model simulation and implementation of two scenarios revealed that the third level of the chain (retail) could be optimized through pre-sorting before delivery to direct supply markets, thereby reducing waste costs." Reducing blocked inventory leads to decreased storage costs, which subsequently resulted in increased market share variability." https://www.jise.ir/article_210617.html Integrating Artificial Intelligence (AI) with Enterprise Resource Planning (ERP) systems is revolutionizing organizational operations, driving the evolution toward intelligent enterprises. This study explores the synergy between AI and ERP, illustrating how their combined capabilities enhance decision-making, streamline processes, and foster organizational maturity. AI-powered analytics, predictive modeling, and automation transform traditional ERP frameworks into adaptive ecosystems that respond dynamically to business needs. Intelligent enterprises achieve unparalleled agility and resilience by aligning operational efficiency with strategic objectives. The paper investigates critical success factors, challenges, and opportunities in this transformation, emphasizing the role of data integration, process reengineering, and workforce adaptation. A roadmap is proposed for organizations to navigate the maturity spectrum, from initial AI adoption to achieving a brilliant enterprise model. The findings underscore that leveraging AI within ERP systems is not just a technological upgrade but a strategic imperative for sustaining competitiveness in the digital era. https://www.jise.ir/article_214277.html This study presents an integrated financing model for the multi-period and multi-product production planning problem, focusing on small and medium-sized enterprises (SMEs) within the supply chain. The rapid globalization and intense competition in the manufacturing sector underscore the importance of efficient production planning and control. Effective production planning integrates various production activities to minimize unproductive resource use, balancing inventory, production costs, and service levels. The study addresses a significant challenge SMEs face: securing adequate financing for their operations. Established companies often have fixed assets that can be used as collateral for loans, whereas SMEs, especially startups, struggle due to limited fixed assets, facing high interest rates if they can secure loans. Additionally, SMEs often have weak bargaining power and limited credit histories, making it difficult to obtain trade credit. To tackle these issues, this research develops a mathematical model that integrates financial flows with physical production flows within the supply chain. The proposed model leverages accounts receivable financing, wherein financial institutions discount receivables to provide necessary funding with fewer collateral requirements. This approach addresses SMEs' liquidity needs and stabilizes their profitability and operational efficiency in uncertain environments. The study employs fuzzy Delphi and hierarchical analysis to identify key parameters and assumptions for the financial and production planning model. It then tests the model using real-world data from SMEs in the sugarcane industry, demonstrating its effectiveness in reducing overall system costs and enhancing financial stability. https://www.jise.ir/article_215089.html In today's complex and uncertain global landscape, supply chain disruptions pose significant challenges to businesses. This study presents an AI-enabled risk management framework that integrates mathematical modeling and metaheuristic optimization to enhance supply chain resilience. A multi-objective optimization model is developed to minimize total costs while mitigating risks associated with supplier reliability, transportation uncertainties, and disruption scenarios. The study employs three advanced optimization algorithms: Genetic Algorithm (GA), Non-Dominated Sorting Genetic Algorithm II (NSGAII), and the recently developed Greedy Man Optimization Algorithm (GMOA). Comparative analysis reveals that GMOA outperforms traditional algorithms in achieving near-optimal solutions with faster convergence. Sensitivity analysis further highlights the critical impact of AI-driven decision-making on risk mitigation. This research provides valuable insights for supply chain managers and policymakers, emphasizing the role of AI-driven optimization in ensuring sustainable and adaptive supply chains. https://www.jise.ir/article_216430.html The increasing demand for efficient and sustainable last-mile delivery has driven the need for intelligent logistics solutions. This study explores the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in last-mile delivery within a Closed-Loop Supply Chain (CLSC) to enhance route optimization, predictive maintenance, and reverse logistics efficiency. By leveraging Deep Q-Network (DQN) reinforcement learning for real-time route planning, Gradient Boosting Machine (GBM) models for predictive maintenance, and multi-objective genetic algorithms (NSGA-II) for reverse logistics, this research develops a comprehensive AI-IoT framework. The system is validated through a real-world case study, demonstrating significant improvements in delivery efficiency, fuel consumption, carbon emissions, and return logistics performance. The findings highlight the transformative potential of AI and IoT in optimizing last-mile delivery, reducing environmental impact, and advancing sustainable supply chain practices. https://www.jise.ir/article_216432.html The integration of artificial intelligence (AI) and machine learning (ML) in predictive maintenance (PM) has transformed maintenance strategies, optimized operational efficiency while supported green supply chain (GSC) sustainability. Traditional maintenance methods, including reactive and preventive maintenance, often lead to excessive costs, energy waste, and unplanned downtime. This study explores the application of AI-driven predictive maintenance using advanced machine learning models, such as Long Short-Term Memory (LSTM), Deep Q-Learning (DQL), and digital twin simulation, to enhance maintenance scheduling and sustainability. Findings reveal that AI-based predictive maintenance reduces downtime by up to 59.2%, cuts energy consumption by 27.6%, and lowers maintenance costs by 35.4%, significantly improving supply chain resilience. This research contributes to the development of AI-powered predictive maintenance frameworks, optimizing both economic and environmental performance in industrial operations. The study also highlights challenges and future research directions, particularly in model interpretability and scalability. https://www.jise.ir/article_217668.html In recent years, the demand for mental health services has significantly increased, accompanied by a rise in the severity of mental health cases. This has prompted mental healthcare organizations to identify key variables affecting service delivery, leading to challenges in effectively allocating human resources. While previous studies have explored the influence of therapist performance on patient treatment outcomes, they have not specifically analyzed the impact of assigning each patient to a high-performing therapist based on their specific condition. This research proposes a data-driven optimization model and performance assessment approach to enhance therapist allocation, integrating empirical data, machine learning algorithms, and mathematical optimization techniques. The findings of this study highlight the significance of caregivers in psychiatric settings, suggesting that patients benefit more from being matched with the appropriate therapists for their disorders. The proposed model enhances the effectiveness and efficiency of mental health therapy, leading to a 4.9% improvement in patient outcomes and more equitable access to quality care. https://www.jise.ir/article_220126.html This study aims to identify and rank the key impediments to blockchain implementation in Iran's banking industry. Despite its potential to enhance transparency, security, and operational efficiency, blockchain adoption faces multiple challenges. To systematically analyze these barriers, a mixed-method approach combining the Fuzzy Delphi Method (FDM) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique was employed. Initially, expert interviews and a literature review were conducted to identify relevant barriers. Subsequently, the FDM was applied to assess their significance based on expert opinions, followed by the DEMATEL technique to determine the causal relationships and prioritize the barriers accordingly. The study targeted 30 industry experts, including banking managers, technology specialists, and blockchain professionals, selected through purposive sampling based on their expertise. Findings revealed that scalability limitations, high implementation costs, and regulatory uncertainty represent the most critical challenges. Moreover, the causal analysis indicated that integration with legacy systems and data privacy concerns serve as root causes, whereas resistance to change and high initial investment costs emerge as consequence-driven barriers. These insights highlight the need for strategic interventions to address fundamental obstacles and facilitate blockchain adoption in Iran’s banking sector. https://www.jise.ir/article_244404.html In this research, the nonlinear seismic behavior of the soil–foundation interaction system under heavy structures has been investigated. A numerical model was developed in Abaqus software, where the contact behavior between soil and foundation was simulated considering slippage and shear yielding. To examine the effect of structure mass, analyses were conducted for masses ranging from 200 to 1000 tons under the recorded El Centro earthquake excitation. Output parameters, including horizontal displacement, foundation rotation, interface shear stress, and foundation settlement, were extracted and analyzed as functions of time. The results indicated that as the structure mass increases, the amplitude of horizontal displacement and foundation rotation decreases, while the static settlement and interface shear stress significantly rise. This response stems from the increased effective vertical force and contact pressure beneath the foundation, leading to enhanced frictional resistance and consequently reduced relative movement. The hysteresis loops obtained from the analyses demonstrate considerable energy dissipation at the soil–foundation interface, with greater intensity observed in heavier structures. Ultimately, by performing sensitivity analyses over the mass range of 200 to 1000 tons, power regression relations were derived between seismic responses and structure mass. These relationships can be utilized for rapid estimation of soil–foundation behavior during the preliminary design phases of heavy structures. The findings highlight that incorporating nonlinear soil–structure interaction plays a critical role in realistic prediction of dynamic response and long-term settlement control in the seismic design of deep foundations. https://www.jise.ir/article_244405.html Enhancing the productivity of financial markets plays a key role in the economic development of countries. Considering the bank-oriented nature of the financial systems in most nations, including Iran, identifying effective methods for measuring and improving productivity levels in banks is of great importance. Accordingly, the present study aims to examine the short-run and long-run causal relationships among capital adequacy, labor force, and total factor productivity (TFP) within a selected sample of ten banks listed on the Tehran Stock Exchange over the period 2010–2017 (1389–1396 in the Iranian calendar). The research results, obtained using the Dynamic Ordinary Least Squares (DOLS), the Vector Error Correction Model (VECM), and the Wald test, reveal a one-way causal relationship from capital adequacy to total factor productivity in the short run. In the long run, however, bidirectional and statistically significant positive relationships are found between labor force and capital adequacy with total factor productivity. Moreover, the error correction term, which represents the speed of short-run adjustment toward long-run equilibrium, is also evaluated. https://www.jise.ir/article_244407.html In recent decades, alongside the expansion of globalization, examining relationship between foreign trade and the mobility of factors of production has become a central issue in international economics. According to Mundell’s theory, trade and factor mobility can be substitutes for one another; however, recent empirical evidence, particularly in emerging economies, points to the existence of a complementary relationship between the two. objective of this study is to investigate the impact of foreign trade on labor and capital mobility in the BRICS member countries over the period 2000–2025 and to empirically test the validity of Mundell’s theory in these countries. To achieve this objective, annual data extracted from the World Development Indicators (WDI) database are employed, and panel econometric methods are used. After testing for stationarity and cointegration among the variables, long-run coefficients are estimated using Fully Modified Ordinary Least Squares (FMOLS) method to analyze the long-term effects of trade liberalization and tariffs on factor mobility. results indicate that trade tariffs have a positive effect on labor mobility and a negative and statistically significant effect on capital mobility, while an increase in trade openness leads to a reduction in labor mobility and a strengthening of foreign direct investment flows. These findings suggest that the relationship between foreign trade and factor mobility in BRICS countries is not necessarily substitutive and exhibits a complementary nature in the case of capital. results emphasize the key role of trade policies in shaping factor mobility and need for coordination among trade, labor market, and investment policies. https://www.jise.ir/article_245163.html Artificial intelligence (AI) has emerged as a transformative technology that enables organizations to deliver highly personalized marketing experiences and improve customer interactions in digital environments. This study investigates the effects of AI-driven personalization on consumer behavior, focusing on customer engagement, purchase intention, customer satisfaction, and brand loyalty. A quantitative research approach was employed using survey data collected from consumers who regularly interact with AI-enabled digital marketing platforms. The proposed conceptual framework was tested using Structural Equation Modeling (SEM) to examine the relationships among the study variables. The findings reveal that AI-driven personalization has significant positive effects on customer engagement, purchase intention, and customer satisfaction. Furthermore, customer engagement, purchase intention, and customer satisfaction were found to positively influence brand loyalty, with customer satisfaction demonstrating the strongest impact. The results suggest that personalized experiences generated through artificial intelligence technologies enhance consumer perceptions, improve purchasing decisions, and strengthen long-term relationships between consumers and brands. The study contributes to the growing literature on artificial intelligence and marketing by providing empirical evidence regarding the behavioral outcomes of AI-based personalization. In addition, the findings offer practical implications for organizations seeking to leverage AI technologies to improve customer experiences and achieve sustainable competitive advantages in increasingly competitive digital marketplaces. https://www.jise.ir/article_222018.html The adoption of 3D printing technology is rapidly reshaping industries by driving operational efficiency, enhancing equipment reliability, and optimizing costs. This study investigates the comprehensive impacts of 3D printing on equipment reliability, human performance, and operational costs using a system dynamics (SD) methodology. Through the development of an SD model, this research examines the intricate causal relationships among critical variables, including equipment performance, workforce skills, training requirements, and cost dynamics. The model simulates these interdependencies over time, offering a dynamic understanding of the long-term effects of 3D printing integration. The findings reveal that 3D printing technology significantly improves equipment reliability by reducing failure rates and optimizing production workflows. Additionally, the reliance on a highly skilled workforce increases as employees require specialized training to operate and maintain advanced 3D printing systems. This, in turn, enhances human reliability and minimizes operational risks. Furthermore, the technology enables substantial cost reductions by decreasing material waste, cutting energy consumption, and improving overall production efficiency. This research provides actionable insights for managers and policymakers, demonstrating the strategic benefits of adopting 3D printing in manufacturing environments. The study highlights the importance of targeted workforce development and investment in advanced technologies to maximize the advantages of 3D printing. https://www.jise.ir/article_222020.html This study endeavours to introduce a novel model for assessing the agility scope of managers within the banking sector of Iran. The agility scope of branch managers delineates the extent to which agility can be attained within the constraints and authorities inherent to their roles. Specifically, our research centres on devising a comprehensive multi-criteria decision-making (MCDM) framework within a probabilistic and fuzzy environment to effectively gauge managers' agility. To this end, we initially define the boundaries of our investigation by identifying the dimensions of agility. Subsequently, we pinpoint potential areas of change through a combination of established models and expert insights. Experts are tasked with enumerating plausible responses to anticipated environmental shifts. Leveraging the Bayesian best-worst approach (BWM) as an MCDM technique, we rank these criteria, while employing Fuzzy simple additive weighting (FSAW) to prioritize responses. Through this innovative methodology, we delineate the domain of managers' agility. Our findings reveal that managers within the studied bank branches exhibit a 55% level of agility. Moreover, we ascertain that authority levels, utility, and response speed significantly influence managers' agility, with changes in these factors exerting a notable impact. The outcomes of this research offer valuable insights for decision-makers, facilitating the optimization of authority allocations to branch managers and thereby enhancing organizational agility. https://www.jise.ir/article_222021.html Evaluating the performance and efficiency of similar units of an organization with the DEA (Data Envelopment Analysis) model has been a hot debate among researchers in recent decades. In this research, for evaluation of the performance and efficiency of Provincial Gas Companies in Iran, first CCR (Charnes, Cooper, and Rhodes) Input-Oriented Multiple Model and AP (Anderson-Peterson) Model was analyzed for ranking efficient units in the formant of DEA; But weakness of models was determined in terms of separating efficiency of companies. In DEA model, units whose efficiency score is equal to "1" may not be ranked through classical DEA methods; In other words, DEA does not differentiate between such units. To solve this problem, the AP approach is proposed to classify efficient units. This problem is generalizable because of the lower quantity of units in comparison with the ‌input and output quantities of the model. In the continuation of this study, for to solve this problem and analysis and evaluation of the efficiency of companies, attitudes including Performance Calculator Neural Networks were used with the units clustering attitude in the format of synthetic models of DEA and ANNs (Artificial Neural Networks) as called Neuro-DEA. Analytical results of calculating efficiency of these models indicated the higher power of calculation and separability of the model for companies in terms of efficiency. The superiority of neural data envelopment analysis model (Neuro-DEA) compared to other models is in minimizing the inputs to the desired output level. In order to measure the efficiency of the.. https://www.jise.ir/article_227274.html The orchestration of a multimodal transport network, intelligently considering fleet capacity dynamics, emerges as a pivotal strategy for managing the intricate logistics of product distribution. Furthermore, the integration of information technology (IT)-based methods stands out as a key enabler, capable of not only enhancing overall operational efficiency but also mitigating superfluous costs within the distribution network This paper introduces a novel paradigm in the form of a strategically designed time window for delivering goods to customers, representing a significant stride towards heightened customer satisfaction. What sets this approach apart is its treatment of customer demands as fuzzy data, acknowledging the inherent uncertainty in demand forecasting. Additionally, the incorporation of a budget constraint further fortifies the practical relevance of the proposed solution, aligning it with the complex realities of supply chain management. To tackle the optimization challenge at hand, the paper advocates for the deployment of two innovative algorithms—the Gray Wolf Optimization Algorithm and the Grasshopper Optimization Algorithm. In its final stride, the paper recommends a case study closely aligned with the presented model, serving as a real-world validation of the proposed strategies. This case study not only bolsters the practical applicability of the model but also serves as a platform for deriving nuanced managerial insights. The results derived from the case study offer valuable perspectives, empowering decision-makers in the realm of supply chain management with actionable intelligence. This paper significantly contributes to the optimization of product distribution networks by innovative strategies, accounting for fuzzy demand data, and addressing budget constraints. https://www.jise.ir/article_230973.html The construction industry, which constitutes a significant portion of global GDP (Gross Domestic Product), profoundly impacts many countries' economies. Global construction spending is projected to reach $14 trillion by 2025. However, the industry faces challenges such as low productivity and quality issues. The digital supply chain and related technologies, such as Building Information Modeling (BIM), blockchain, and the Internet of Things (IoT), can help streamline processes and address these problems. This study aims to identify and analyze the critical components of the digital supply chain in Iran's construction industry and determine the causal relationships between them. The Meta-synthesis Method and Decision Making Trial and Evaluation Laboratory (DEMATEL) are employed to achieve this. The research results identify key components and their relationships, which can contribute to improving the quality and efficiency of the construction supply chain, as well as accelerating the digitalization process in the industry. https://www.jise.ir/article_230975.html The power plant industry plays a pivotal role in national development and contributes significantly to value creation across supply chains. In the context of global sustainability challenges, ensuring both resilience and sustainability in supply chain management has become a strategic necessity. This study aims to identify and prioritize the key factors influencing supply chain resilience within the power plant sector. Drawing upon previous research on sustainable supply chain models, a comprehensive set of factors was identified and classified into paradigms of leanness, agility, resilience, and greenness, alongside economic, social, and environmental dimensions. Using the Analytic Hierarchy Process (AHP), these factors were systematically prioritized, highlighting the environmental dimension as the foremost driver of sustainable supply chain performance in the industry. The findings provide practical insights for decision-makers to allocate resources effectively, strengthen resilience, and align strategies with sustainable development goals, ensuring competitiveness in both domestic and international markets. https://www.jise.ir/article_232602.html This study presents a novel method for regulating the target price of a new automotive product, combining deep learning, game theory, and control theory to enhance pricing strategies. Unlike existing pricing methods, our framework uniquely combines CNN-based cost feature extraction, GAN-driven design optimization, and RL-based feedback control in a unified hyperstructure. Our approach employs Convolutional Neural Networks (CNNs) to analyze cost features extracted from Product Lifecycle Management (PLM) data, demonstrating a significant reduction in analysis time by 30% compared to traditional methods. A Generative Adversarial Network (GAN) aids in the effective management of design options, optimizing design costs by up to 25%. Reinforcement learning within a learning-based control framework enables convergence to Nash equilibrium. This integration results in optimal pricing strategies that align target costs with market demands, increasing projected profitability by 15% over standard pricing models. This research highlights the innovative application of multidisciplinary techniques in automotive pricing. https://www.jise.ir/article_232603.html Sustainable supply chain management (SSCM) has become the key concept for every industry in managing their supply chain system by focusing on three aspects: economics, social, and environmental. Even though the implementation of SSCM will help the industry increase the efficiency of supply chain management, some challenges make the firms cannot implement the SSCM concept well and unsuccessful. Although research has examined several SSCM viewpoints, the barriers that prevent emerging economies from adopting SSCM in the textile sector to meet the Sustainable Development Goals (SDGs) are not sufficiently highlighted in the empirical literature that has already been published. This study analyzes different barriers and investigates how they are interconnected. From the literature research, main barriers were first identified in the process. The barriers were then prioritized in order of significance using a combination of fuzzy theory, Pareto analysis, and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) framework. Finally, the cause-and-effect relationships among these barriers were established. A lack of commitment from the supplier’s top management, insufficient financial incentives, and the absence of supportive government standards and regulations were identified as the three topmost significant barriers to SSCM adoption. These findings reveal the multifaceted impact of policies on SSCM, providing policymakers with a clearer perspective to formulate more precise policies for guiding the direction of SSCM development and accelerating its innovation pace https://www.jise.ir/article_232604.html Measurement system capability analysis (MSCA) is vital for enhancing process and quality performance, yet its use when the system is defined by a simple linear profile (SLP), has not been investigated. Existing studies on SLP MSCA primarily concentrate on profile parameters, establishing precision-tolerance ratio (PTR) criteria for both intercept and slope but neglect the critical influence of specification limits (SLs) on PTR accuracy. This paper presents a novel evaluation framework for SLP measurement systems. By introducing a new method for calculating SLs for profile parameters, the study proposes a modified PTR criterion and extends process capability indices (PCIs) for measurement profiles. Compared to existing approaches, our proposed indices enable a more accurate and comprehensive assessment of measurement system performance by incorporating both accuracy and precision, as well as appropriately determined SLs for profile parameters. A case study in spring manufacturing validates the effectiveness of these indices in industrial contexts. https://www.jise.ir/article_232605.html The great problem of preventive maintenance to maintain operational readiness and keeping equipment, machinery and facility. Use planning system and provide maintenance and repair services, we create the most favorable adopt best practices for organizations with maximum efficiency, sustainability and cost reduction, in the absence of the restrictions on the increase investments of sources and of the There. To use this system needs to identify some of the important factors affecting its performance is always a cost associated beneficial results will be achieved. The research effort is to identify and prioritize the factors influencing them using Analytical Hierarchy Process in the organizations bus Tabriz through the questions and comments of managers and experts have been addressed.According to the obtained results, it is observed that the main criterion of technology with a coefficient of priority (0.1290) has the highest importance among the studied criteria. Also, the criteria of structural problems (0.1230), cultural issues (0.1119) and lack of proper knowledge of the net (0.0986) have the next ranks. Findings: In this regard, 12 sub-factors were identified, which were categorized into the main criteria of technology, structural problems, cultural issues and lack of proper recognition of the net, According to the obtained results, it can be seen that the main criterion of technology has the highest importance among the examined criteria. Also, the criteria of structural problems, cultural issues and lack of proper recognition of net have the next ranks. https://www.jise.ir/article_232606.html Objective: This study analyzes the structure and performance of the co-authorship network in nutrigenomics, aiming to uncover its collaboration dynamics and identify key influential researchers. Method: Data from PubMed (2020–2024) were analyzed using Gephi 0.10.1 and NetworkX 3.4.1 to evaluate the network at macro and micro levels. Macro-level metrics—including density, clustering coefficient, modularity, and diameter—were calculated to assess the overall structure of the network. Micro-level analysis focused on centrality measures (degree, betweenness, eigenvector, and PageRank) to evaluate individual researchers' influence and to generate a ranked list of key contributors. Results: Analysis was conducted on a total of 920 articles extracted from PubMed. The co-authorship network exhibits a highly modular structure (modularity score: 0.965) with 505 distinct communities, reflecting diverse research areas. Leaders in the network are distributed across several countries, with the United States, China, and Spain being the top three. Ordovas JM, Li H, and Wang D emerged as the most prolific authors, holding the highest degree centrality. Ordovas JM, Holscher HD, and Yan Y lead in betweenness centrality, connecting disparate parts of the network, while Ordovas JM, Wang D, and Yan Y have the highest eigenvector centrality, signifying strategic collaborations. PageRank highlights Ordovas JM and El-Sohemy A as the most impactful authors. Conclusion: Nutrigenomics research is rapidly expanding due to its potential in chronic disease prevention. While key researchers enhance network diversity, their centrality makes the network vulnerable to their exit. Promoting collaboration among these researchers and integrating young talent can bolster the network’s resilience and growth. https://www.jise.ir/article_232608.html This study develops a master planning model for supply chain traceability in the Iranian handmade carpet industry using blockchain technology. Iranian handmade carpets, recognized as symbols of Iranian culture and art, face significant challenges, including increasing competition and design imitation. The primary objective of this study is to develop a model that facilitates the traceability of products from production to the final customer, thereby preserving the authenticity and credibility of handmade carpets. The proposed model includes a manufacturer, multiple distribution centers, and various customers, focusing on optimizing the supply chain's total net profit. The results indicate that implementing traceability systems, particularly those utilizing blockchain technology, can enhance profitability, increase market share, and strengthen the position of handmade carpets in international markets. This research presents an innovative solution for improving the competitiveness of the handmade carpet industry on a global scale. https://www.jise.ir/article_233103.html With the rapid expansion of e-commerce and the emergence of innovative payment methods, customer experience on online platforms has become a vital factor for the success of businesses. This study aims to provide a framework for enhancing customers' credit purchasing experience in the BNPL (Buy Now, Pay Later) context, emphasizing a systematic literature review (SLR) and qualitative content analysis. In the first step, a collection of relevant studies was gathered using search strategies in domestic and international scientific databases. Then, by eliminating duplicates and evaluating the selected articles, qualitative data were extracted and analyzed using thematic analysis to identify the dimensions, main components, and influencing sub-components. Finally, a model was designed to illustrate the relationships between these components. The analysis results indicated that the credit purchasing experience on BNPL platforms is influenced by four key dimensions: online shopping experience (including customer trust, ease of use, and customer satisfaction), the nature of credit purchasing (including payment flexibility and behavioral outcomes such as instant purchasing and consumerism), technology acceptance (focusing on individual factors such as financial attitude and innovativeness), and customer experience frameworks (including digital marketing and customer journey design). According to the proposed framework, success in this area requires attention to multiple psychological, behavioral, technological, and marketing dimensions. Finally, practical recommendations were provided for the "SnapPay" platform, including enhancing customer transparency and trust, continuously improving ease of use and user experience, smart management of behavioral outcomes, promoting responsible consumption, and utilizing data for personalizing customer experience and targeted digital marketing. https://www.jise.ir/article_238548.html When a failure occurs in a system, the first essential questions concern the location and severity of the fault, as these factors determine appropriate corrective actions and help maintain continuous operation. Although numerous studies address this topic, most rely heavily on deep system knowledge and are tailored to a limited set of predefined faults. In this research, we propose a generalizable fault-diagnosis model that reduces dependence on expert knowledge and complex analytical procedures, thereby improving system resilience through fast fault localization, severity estimation, and timely intervention. The methodological contribution of this work lies in integrating a fuzzy inference layer with the Random Forest algorithm, enabling the model to combine operator experience with data-driven learning. This hybrid fuzzy–ML structure enhances interpretability through rule-based reasoning while improving robustness in uncertain environments—an approach aligned with recent advances in fuzzy–ML fusion for fault diagnosis. The proposed framework is validated on two quadcopter fault scenarios. The experimental results show that the model provides rapid processing, straightforward implementation, and stable performance under uncertain conditions. These characteristics make the method a practical and accessible tool for both researchers and industrial practitioners. https://www.jise.ir/article_239627.html This study examines the relationship between cash flow management, firm performance, and financial risk among companies listed on the Tehran Stock Exchange. Focusing on operating, investing, financing, and free cash flows, as well as the cash conversion cycle, the research analyzes their effects on performance represented by return on assets (ROA) and risk measured by the standard deviation of stock returns. The study is applied in purpose and follows a descriptive-correlational design. Using a systematic screening method, a sample of 137 firms was selected and analyzed over a six-year period from 2019 to 2024, yielding 822 firm-year observations. Multivariate regression analyses, conducted with EViews 8 software and panel data under fixed effects, were employed to test the hypotheses. The findings demonstrate that operating, investing, financing, and free cash flows have a significant influence on both firm performance and risk, while the cash conversion cycle has a significant impact only on performance, not on risk. These results contribute to a deeper understanding of how diverse aspects of cash flow management affect corporate value and stability in emerging financial markets.