Advanced Decision Making Methods And Applications In System Safety And Reliability Problems
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Advanced Decision-Making Methods and Applications in System Safety and Reliability Problems
This book reviews and presents several approaches to advanced decision-making models for safety and risk assessment. Each introduced model provides case studies indicating a high level of efficiency, robustness, and applicability, which allow readers to utilize them in their understudy risk-based assessment applications. The book begins by introducing a novel dynamic DEMATEL for improving safety management systems. It then progresses logically, dedicating a chapter to each approach, including advanced FMEA with probabilistic linguistic preference relations, Bayesian Network approach and interval type-2 fuzzy set, advanced TOPSIS with spherical fuzzy set, and advanced BWM with neutrosophic fuzzy set and evidence theory. This book will be of interest to professionals and researchers working in the field of system safety and reliability and postgraduate and undergraduate students studying applications of decision-making tools and expert systems.
Progressive Decision-Making Tools and Applications in Project and Operation Management
In today's complex operational environments, leveraging advanced decision-making tools becomes imperative, particularly in uncertain scenarios. This book deepens the nuances of employing state-of-the-art decision-making methodologies within various industrial sectors for optimal project and operations management. The essence of integrating these advanced tools is to equip professionals with pivotal insights for cost-effective management and to strategize against potential operational shortcomings. Furthermore, the methodologies elucidated provide a robust foundation for crafting informed, risk-centric strategies that uphold the integrity of operations across diverse application domains. Readers will discover a rich tapestry of methodologies tailored for engineers and analysts. Deeply rooted in mathematical modeling, these approaches are complemented by human judgment and participation. Fundamental attributes of these methods include the evaluation of alternatives, benchmarking againstcriteria, assigning scores based on varying requirements, and assigning weights to denote the significance of individual criteria vis-à-vis others. The book embarks on a structured journey, commencing with a comprehensive review of evolving decision-making methodologies in project and operations management, enriched by metadata analysis. Subsequent chapters are meticulously organized, each spotlighting a distinct approach. Topics span foundational concepts in decision-making, the nuances of performance metrics in the digital age, and the implications of emerging technologies on operations management. Targeted towards professionals and researchers immersed in project and operations management, this work will also immensely benefit postgraduate and undergraduate students in related fields. Moreover, its relevance extends to professionals across diverse sectors, from oil and gas, marine and offshore, and renewable energies to chemical complexes, manufacturing, and healthcare systems.
Artificial Intelligence in Workplace Health and Safety
In today's dynamic workplace environment, ensuring the safety and well-being of employees has never been more critical. This book explores cutting-edge technologies intersecting with workplace safety to deliver effective and practical results. Artificial Intelligence in Workplace Health and Safety: Data-Driven Technologies, Tools and Techniques offers a comprehensive roadmap for professionals, researchers, and practitioners in work health and safety (WHS), revolutionizing traditional approaches through the integration of data-driven methodologies and artificial intelligence. Covering the foundations and practical applications of data-driven WHS and historical perspectives to current regulatory frameworks, it investigates the key concepts of data collection, management, and integration. Through real-world case studies and examples, readers can discover how AI technologies such as machine learning, computer vision, and natural language processing are reshaping WHS practices, mitigating risks, and optimizing safety measures. The reader will learn applications of AI and data-driven methodologies in their workplace settings to improve safety. With its practical insights, real-world examples, and progressive approach, this title ensures that readers are not just prepared for the future of WHS but empowered to shape it for better. This text is written for professionals and practitioners seeking to enhance workplace safety through innovative technologies. This extends to safety professionals, HR personnel and engineers across different sectors.