Nasser Mikaeilvand

Technological empowerment and resilience play a crucial role in enhancing the capacity of organizations to maintain sustainability and respond effectively to crises and disruptions. This study aimed to examine the growth trend and thematic structure of international research in the field of smart technologies and sustainable production during the period from 2000 to 2024 through a systematic review of 290 scientific research articles indexed in global databases. The findings indicate that until 2018, the growth of publications was very slow, with an annual average of fewer than six articles. However, since 2019, a remarkable acceleration has been observed, with more than 65% of the articles (190 papers) published within the last five years. In terms of quality, the proportion of articles published in Q1 journals in recent years has increased to over 48% (compared to about 16% during the first decade of the study period). Moreover, the journals Journal of Cleaner Production, Sustainability, and International Journal of Production Research showed the highest frequency of publications, and the co-word network of keywords reflects a focus on areas such as sustainable development, Industry 4.0, the Internet of Things (IoT), and artificial intelligence (AI). Structurally, keyword clustering demonstrates the synergy between advanced smart technologies and sustainability objectives in industry. The statistical results of the meta-analysis showed that the Z-effect index was 1.82 (lower than the critical value of 2.69), indicating the stability of the findings. Furthermore, trend analysis reveals that the focus of studies has shifted from theoretical and feasibility issues toward the practical application of IoT and AI in industry and supply chains, with more than 35% of all articles dedicated to these topics. An examination of the cluster distribution of frequently used keywords in the fields of “smart manufacturing,” “smart technologies,” and “sustainable production” indicates that the largest share of articles in recent years falls under the clusters of “smart manufacturing and Industry 4.0” (12%) and “technological innovation and additive manufacturing” (12%). These findings highlight scientific maturity, an increase in international impact, and the growing attention of researchers to technological and resilient approaches in advancing sustainable production and development.

The explosive growth of Internet of Things (IoT) devices has generated considerable data in diverse networks. This poses serious challenges in collecting timely information and managing frequency resources optimally. In particular, unauthorized access, measurement constraints, and variable channel conditions cause interference, performance degradation, and security compromise, especially in distributed IoT systems. This research presents a comprehensive system for improving data collection in heterogeneous IoT networks. Using complex mathematical models and machine learning algorithms, the system aims to increase the efficiency of frequency resource utilization and reduce interference in network access. A Q-based reinforcement learning method is designed along with an intelligent MAC protocol. Simulation results show that this method increases channel utilization efficiency by 25%, reduces interference probability by 30% compared to traditional methods such as ALOHA, and provides a flexible and scalable solution for frequency resource management. The performance of the proposed system is significantly better than traditional methods, increasing channel utilization efficiency by 25% and reducing the probability of interference by 30%. The system's self-learning capability enables effective frequency resource management even in complex and dense environments. This research presents an innovative method for data collection in IoT networks that combines machine learning and mathematical modeling, providing a secure and scalable solution for the next generation of heterogeneous networks. This system paves the way for designing more stable and efficient networks in various fields, including smart cities and industries.

Due to the variable resource availability and diverse user needs, efficient task scheduling in cloud computing has become increasingly important. This study introduces GA-PSO-Min, a novel approach that synergistically combines genetic algorithms (GA), particle swarm optimization (PSO), and Min-Min strategy to improve scheduling efficiency in cloud environments. Unlike conventional approaches that prioritize single criteria, GA-PSO-Min emphasizes multi-objective optimization, minimizing the overall completion time while ensuring scalability and flexibility. The approach leverages the global search capabilities of GA and the fast convergence of PSO to initialize its population with a Min-Min solution, thereby outperforming standalone approaches. Compared to Min-Min, GA-PSO-Min reduces completion time by 2–7% in twelve distinct scenarios, including compute-intensive, I/O-intensive, and mixed workloads. The initial energy reduction is validated through a simple power model. It surpasses Min-Min with a temporal complexity of O(k⋅P⋅n⋅m), achieving a balance between enhanced performance and computational cost. The sensitivity analysis reveals the optimal resilience of the parameters (e.g., an inertia weight of 0.7), confirming GA-PSO-Min as an energy-efficient and scalable solution for modern cloud systems. Subsequent study will encompass improved QoS optimization and empirical validation.

Due to the increasing use of the Internet of Things, efficient task scheduling in cloud computing has become increasingly important with the aim of maximizing the use of available resources, reducing energy consumption, and enhancing the quality of service (QoS). In this paper, we use the Firefly Optimization (FFO) algorithm to improve scheduling efficiency and minimize the overall completion time in cloud environments. For this purpose, twelve distinct scenarios were designed in the Cooja Contiki simulator environment with the perspective of computationally intensive, input/output intensive, and mixed workloads, and the overall completion time results obtained with the Min-Min and GA-PSO-Min methods were compared and the better performance of the method was confirmed.

This study aims to enhance the resilience of the automotive parts supply chain and compare the effectiveness of artificial intelligence techniques, specifically Decision Tree and Support Vector Machine (SVM) models. The research dataset consists of 200 simulated records from various supply chain scenarios. For each sample, indicators such as the number of suppliers, average delivery time, safety stock, disruption frequency, and response speed were measured. Model performance was evaluated based on metrics including Accuracy, Recall, F1-Score, and the Confusion Matrix. The results revealed that the Decision Tree model, with an accuracy of 0.92, recall of 0.91, and F1-score of 0.92, demonstrated superior classification capability compared to SVM. While SVM achieved close performance with an accuracy of 0.91 and recall of 0.90, it was less effective in terms of interpretability and decision-making transparency. Additionally, in terms of AUC in the ROC curve and the Precision–Recall metric, the Decision Tree model outperformed the SVM. Beyond its higher accuracy, the Decision Tree model offered greater advantages in identifying influential factors affecting supply chain resilience and in providing transparent decision-making pathways. In contrast, SVM proved more effective in analyzing complex patterns and nonlinear data, although it suffered from lower interpretability. Overall, the findings of this study confirm that artificial intelligence techniques contribute to improved resilience, risk management, and decision optimization in the automotive parts supply chain. Based on the results, it is recommended to implement policies such as supplier diversification, intelligent safety stock management, and enhancement of disruption response speed to bolster the supply chain's robustness against diverse challenges.

Supply chain resilience refers to organizations’ ability to maintain stability and respond effectively to crises and sudden disruptions. As supply chains become increasingly complex, artificial intelligence (AI) has emerged as a highly efficient analytical tool. Through data analysis, pattern prediction, and risk identification, AI enables companies to respond promptly and optimally to critical conditions. This study systematically reviews and performs a bibliometric analysis of 238 review articles published between 2000 and 2024, examining the growth trends and thematic structure of research in the field of "artificial intelligence and supply chain resilience." Findings indicate that over 65% of the articles were published in the past five years (2019–2023), with the primary research focus centered on "artificial intelligence and machine learning." Keyword co-occurrence network analysis using scientometric indices reveals that the phrase “Internet of Things” holds the highest centrality score and is positioned at the center of the network. On average, each frequently used keyword is linked to more than 15 other concepts, with the network’s average degree estimated between 8 and 12. Thematic clustering of the research corpus reveals the formation of seven prominent research clusters, including "smart agriculture," "cold chain logistics," and "energy management." Among these, the cluster of "artificial intelligence and machine learning" has the highest share, indicating a strong research focus in recent years. The publication bias test yielded a Z-value of 1.614 (less than the critical value of 2.39 at the 0.05 significance level), indicating no significant bias in the data and thus affirming the validity of the current analysis. Moreover, based on practical study results, the implementation of AI algorithms has led to an average reduction of approximately 18% in operational supply chain costs and an increase of up to 25% in recovery speed following disruptions. These numerical and structural findings demonstrate that artificial intelligence—especially in interaction with the Internet of Things and machine learning—has played a pivotal and catalytic role in research related to supply chain resilience and is expected to continue to do so in the future. The clustered studies in the domain of supply chain resilience using artificial intelligence identified seven main clusters with significant differences in participation and growth trends. The largest cluster, “artificial intelligence and machine learning,” accounts for approximately 35% of all articles and has experienced a remarkable growth surge, particularly since 2019.