The flexible job shop scheduling problem is a complex NP-hard challenge critical to modern manufacturing systems, but existing research predominantly focuses on low-dimensional optimization, practical scenarios often require balancing high-dimensional conflicting objectives such as energy consumption, machine load, tardiness, and completion time. Current -objective algorithms struggle to maintain convergence and diversity in such high-dimensional spaces, leading to suboptimal Pareto fronts. To address this problem, we propose an improved NSGA-III (WDHS-NSGA-III) integrating a reference vector-guided subpopulation partitioning mechanism and a density-aware hybrid search strategy. The algorithm divides populations into subspaces using reference vectors to enable adaptive cross-subpopulation exploration, then applies localized neighborhood searches with five operators to prioritize under-explored regions. Experiments on benchmark instances demonstrate that WDHS-NSGA-III significantly outperforms commonly used methods, achieving balanced optimization across most objectives. The results validate its robustness in resolving high-dimensional tradeoffs, offering a valid solution for smart manufacturing scheduling.
Coral reefs are vital marine ecosystems that play a crucial role in supporting biodiversity, providing coastal protection, and driving global fisheries and tourism economies. However, these ecosystems are under increasing threat from coral bleaching, driven by climate change and human-induced stressors. Monitoring and mitigating coral bleaching require scalable, automated systems capable of accurate detection and classification. This study presents a synthesis of state-of-the-art deep learning models, including VGG16 and YOLO, for coral health assessment. Additionally, a novel annotated coral bleaching database is introduced, designed from diverse video and image sources to train machine learning models effectively. Our results demonstrate that these approaches not only improve the accuracy of coral health classification but also enhance the scalability and reliability of reef monitoring efforts, supporting marine conservation initiatives. Future work focuses on expanding datasets and exploring advanced architectures such as ResNet50 and EfficientNet to improve generalization and robustness.
This paper identifies and presents accelerated and automated AI solutions for major knowledge management problems in complex systems, especially in defense. From the interviews and surveys conducted on defense personnel and using defending-against-knowledge-loss triangulation, the study points to the following enduring issues: paperwork, siloed knowledge, security measures, and knowledge obsolescence due to employee turnover. The paper uses thematic analysis using the MAXQDA qualitative data analysis to elucidate the identified inefficiencies as barriers to decision-making and collaborative work that threaten organizational outcomes. In response to these issues, the study seeks to develop a model offering new solutions for the mechanised adoption of knowledge management processes, including knowledge capture, distribution, and enhancement of interoperability to support structured knowledge sharing. This model incorporates AI functionalities to automate processes and minimise paperwork to reduce decision-making latency and advance cross-departmental knowledge flow with the added advantage of accessing real-time information while conforming to the security requirements through operational access privileges.
Machine learning algorithms have been widely adopted across a variety of application domains. However, training models and evaluating their performance is often time-consuming and computationally expensive. Furthermore, the identification of (near-)optimal learning strategies, including design choices and hyperparameter configurations, remains a significant challenge, as these decisions substantially influence the quality of learning outcomes. To address these issues, Automated Machine Learning (AutoML) has been developed with the aim of minimizing manual intervention and automating the creation of ready-to-use machine learning pipelines. Despite the widespread adoption of numerous successful AutoML systems, their performance is observed to vary across different datasets and learning scenarios. Algorithm selection has been proposed as a solution to this limitation, enabling the recommendation of algorithms on a per-instance basis. In this study, algorithm selection is utilized to enhance the capabilities of existing AutoML frameworks with minimal additional effort. A comprehensive empirical evaluation is conducted on 39 diverse tasks from the OpenML platform, involving 6 state-of-the-art AutoML methods. The results demonstrate that the integration of algorithm selection not only amplifies the strengths of current AutoML systems but also leads to improved performance and robustness. These findings highlight the practicality and efficacy of algorithm selection as a critical advancement for the next generation of AutoML technologies.
The resilience of the pharmaceutical supply chain (PSC) is crucial to ensure the continuous availability of essential medicines and other products, particularly during logistics disruptions. This article presents a systematic review of the literature on machine learning (ML) and artificial intelligence (AI) techniques and their importance to PSC resilience. By analyzing existing literature from multiple databases, including Web of Science and IEEE Xplore, over the past five years, we identified key areas where ML and AI have been effectively utilized. These areas include demand forecasting, risk management, and inventory optimization. This review also highlights significant research gaps and proposes future directions for investigation. Our findings suggest that while ML and AI offer promising solutions for improving supply chain resilience, there is a need for more studies that integrate various ML and AI approaches into PSC. Our analysis reveals that there are no clear government regulations related to the usage of ML or AI in PSC, no robust real-world applications addressing the challenges of adopting these technologies, and no clear predictive models to assess their impact on PSC resilience. This ongoing work aims to provide a foundation for future research, ultimately fostering more adaptive and resilient PSCs.
