COMPUTATIONAL INTELLIGENCE IN SUBSTAINABLE RELIABILITY ENGINEERING The book is a comprehensive guide on how to apply computational intelligence techniques for the optimization of sustainable materials and reliability engineering. This book focuses on developing and evolving advanced computational intelligence algorithms for the analysis of data involved in reliability engineering, material design, and manufacturing to ensure sustainability. Computational Intelligence in Sustainable Reliability Engineering unveils applications of different models of evolutionary algorithms in the field of optimization and solves the problems to help the manufacturing industries. Some special features of this book include a comprehensive guide for utilizing computational models for reliability engineering, state-of-the-art swarm intelligence methods for solving manufacturing processes and developing sustainable materials, high-quality and innovative research contributions, and a guide for applying computational optimization on reliability and maintainability theory. The book also includes dedicated case studies of real-life applications related to industrial optimizations. Audience Researchers, industry professionals, and post-graduate students in reliability engineering, manufacturing, materials, and design.

Computational intelligence is rapidly becoming an essential part of reliability engineering. This book offers a wide spectrum of viewpoints on the merger of technologies. Leading scientists share their insights and progress on reliability engineering techniques, suitable mathematical methods, and practical applications. Thought-provoking ideas are embedded in a solid scientific basis that contribute to the development the emerging field. This book is for anyone working on the most fundamental paradigm-shift in resilience engineering in decades. Scientists benefit from this book by gaining insight in the latest in the merger of reliability engineering and computational intelligence. Businesses and (IT) suppliers can find inspiration for the future, and reliability engineers can use the book to move closer to the cutting edge of technology.

The text comprehensively discusses computational models including artificial neural networks, agent-based models, and decision field theory for reliability engineering. It will serve as an ideal reference text for graduate students and academic researchers in the fields of industrial engineering, manufacturing engineering, computer engineering, and materials science. Discusses the development of sustainable materials using metaheuristic approaches. Covers computational models such as agent-based models, ontology, and decision field theory for reliability engineering. Presents swarm intelligence methods such as ant colony optimization, particle swarm optimization, and grey wolf optimization for solving the manufacturing process. Include case studies for industrial optimizations. Explores the use of computational optimization for reliability and maintainability theory. The text covers swarm intelligence techniques including ant colony optimization, particle swarm optimization, cuckoo search, and genetic algorithms for solving complex industrial problems of the manufacturing industry as well as predicting reliability, maintainability, and availability of several industrial components.

This book offers insight into the current issues of the merger between reliability engineering and computational intelligence. The intense development of information technology allows for designing more complex systems as well as creating more detailed models of real-world systems which forces traditional reliability engineering approaches based on Boolean algebra, probability theory, and statistics to embrace the world of data science. The works deal with methodological developments as well as applications in the development of safe and reliable systems in various kinds of distribution networks, in the development of highly reliable healthcare systems, in finding weaknesses in systems with the human factor, or in reliability analysis of large information systems and other software solutions. In this book, experts from various fields of reliability engineering and computational intelligence present their view on the risks, the opportunities and the synergy between reliability engineering and computational intelligence that have been developed separately but in recent years have found a way to each other. The topics addressed include the latest advances in computing technology to improve the real lives of millions of people by increasing safety and reliability of various types of real-life systems by increasing the availability of software services, reducing the accident rate of means of transport, developing high reliable patient-specific health care, or generally, save cost and increase efficiency in the work and living environment. Though this book, the reader has access to professionals and researchers in the fields of reliability engineering and computational intelligence that share their experience in merging the two as well as an insight into the latest methods, concerns and application domains.

HANDBOOK OF INTELLIGENT COMPUTING AND OPTIMIZATION FOR SUSTAINABLE DEVELOPMENT This book provides a comprehensive overview of the latest breakthroughs and recent progress in sustainable intelligent computing technologies, applications, and optimization techniques across various industries. Optimization has received enormous attention along with the rapidly increasing use of communication technology and the development of user-friendly software and artificial intelligence. In almost all human activities, there is a desire to deliver the highest possible results with the least amount of effort. Moreover, optimization is a very well-known area with a vast number of applications, from route finding problems to medical treatment, construction, finance, accounting, engineering, and maintenance schedules in plants. As far as optimization of real-world problems is concerned, understanding the nature of the problem and grouping it in a proper class may help the designer employ proper techniques which can solve the problem efficiently. Many intelligent optimization techniques can find optimal solutions without the use of objective function and are less prone to local conditions. The 41 chapters comprising the Handbook of Intelligent Computing and Optimization for Sustainable Development by subject specialists, represent diverse disciplines such as mathematics and computer science, electrical and electronics engineering, neuroscience and cognitive sciences, medicine, and social sciences, and provide the reader with an integrated understanding of the importance that intelligent computing has in the sustainable development of current societies. It discusses the emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative intelligent techniques in a variety of sectors, including IoT, manufacturing, optimization, and healthcare. Audience It is a pivotal reference source for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in the field of artificial intelligence in the areas of Internet of Things, renewable energy, optimization, and smart cities.

Power system reliability is the focus of intensive study due to its critical role in providing energy supply to modern society. This comprehensive book describes application of some new specific techniques: universal generating function method and its combination with Monte Carlo simulation and with random processes methods, Semi-Markov and Markov reward models and genetic algorithm. The book can be considered as complementary to power system reliability textbooks.

Computational Intelligence (CI) is one of the most important powerful tools for research in the diverse fields of engineering sciences ranging from traditional fields of civil, mechanical engineering to vast sections of electrical, electronics and computer engineering and above all the biological and pharmaceutical sciences. The existing field has its origin in the functioning of the human brain in processing information, recognizing pattern, learning from observations and experiments, storing and retrieving information from memory, etc. In particular, the power industry being on the verge of epoch changing due to deregulation, the power engineers require Computational intelligence tools for proper planning, operation and control of the power system. Most of the CI tools are suitably formulated as some sort of optimization or decision making problems. These CI techniques provide the power utilities with innovative solutions for efficient analysis, optimal operation and control and intelligent decision making. This edited volume deals with different CI techniques for solving real world Power Industry problems. The technical contents will be extremely helpful for the researchers as well as the practicing engineers in the power industry.

Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering illustrates the concepts of risk, reliability analysis, its estimation, and the decisions leading to sustainable development in the field of civil and environmental engineering. The book provides key ideas on risks in performance failure and structural failures of all processes involved in civil and environmental systems, evaluates reliability, and discusses the implications of measurable indicators of sustainability in important aspects of multitude of civil engineering projects. It will help practitioners become familiar with tolerances in design parameters, uncertainties in the environment, and applications in civil and environmental systems. Furthermore, the book emphasizes the importance of risks involved in design and planning stages and covers reliability techniques to discover and remove the potential failures to achieve a sustainable development. - Contains relevant theory and practice related to risk, reliability and sustainability in the field of civil and environment engineering - Gives firsthand experience of new tools to integrate existing artificial intelligence models with large information obtained from different sources - Provides engineering solutions that have a positive impact on sustainability

This book presents the latest developments in both qualitative and quantitative computational methods for reliability and statistics, as well as their applications. Consisting of contributions from active researchers and experienced practitioners in the field, it fills the gap between theory and practice and explores new research challenges in reliability and statistical computing. The book consists of 18 chapters. It covers (1) modeling in and methods for reliability computing, with chapters dedicated to predicted reliability modeling, optimal maintenance models, and mechanical reliability and safety analysis; (2) statistical computing methods, including machine learning techniques and deep learning approaches for sentiment analysis and recommendation systems; and (3) applications and case studies, such as modeling innovation paths of European firms, aircraft components, bus safety analysis, performance prediction in textile finishing processes, and movie recommendation systems. Given its scope, the book will appeal to postgraduates, researchers, professors, scientists, and practitioners in a range of fields, including reliability engineering and management, maintenance engineering, quality management, statistics, computer science and engineering, mechanical engineering, business analytics, and data science.