Metaheuristic Algorithms In Industry 4 0

Due to increasing industry 4.0 practices, massive industrial process data is now available for researchers for modelling and optimization. Artificial Intelligence methods can be applied to the ever-increasing process data to achieve robust control against foreseen and unforeseen system fluctuations. Smart computing techniques, machine learning, deep learning, computer vision, for example, will be inseparable from the highly automated factories of tomorrow. Effective cybersecurity will be a must for all Internet of Things (IoT) enabled work and office spaces. This book addresses metaheuristics in all aspects of Industry 4.0. It covers metaheuristic applications in IoT, cyber physical systems, control systems, smart computing, artificial intelligence, sensor networks, robotics, cybersecurity, smart factory, predictive analytics and more. Key features: Includes industrial case studies. Includes chapters on cyber physical systems, machine learning, deep learning, cybersecurity, robotics, smart manufacturing and predictive analytics. surveys current trends and challenges in metaheuristics and industry 4.0. Metaheuristic Algorithms in Industry 4.0 provides a guiding light to engineers, researchers, students, faculty and other professionals engaged in exploring and implementing industry 4.0 solutions in various systems and processes.

Artificial Intelligence in Industry 4.0 and 5G Technology Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors As the pace of Artificial Intelligence (AI) technology innovation continues to accelerate, identifying the appropriate AI capabilities to embed in key decision processes has never been more critical to establishing competitive advantage. New and emerging analytics tools and technologies can be configured to optimize business value, change how an organization gains insights, and significantly improve the decision-making process across the enterprise. Artificial Intelligence in Industry 4.0 and 5G Technology helps readers solve real-world technological engineering optimization problems using evolutionary and swarm intelligence, mathematical programming, multi-objective optimization, and other cutting-edge intelligent optimization methods. Contributions from leading experts in the field present original research on both the theoretical and practical aspects of implementing new AI techniques in a variety of sectors, including Big Data analytics, smart manufacturing, renewable energy, smart cities, robotics, and the Internet of Things (IoT). Presents detailed information on meta-heuristic applications with a focus on technology and engineering sectors such as smart manufacturing, smart production, innovative cities, and 5G networks. Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor. Provides guidance on implementing metaheuristics in different applications and hybrid technological systems. Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement. Artificial Intelligence in Industry 4.0 and 5G Technology is a valuable resource for IT specialists, industry professionals, managers and executives, researchers, scientists, engineers, and advanced students an up-to-date reference to innovative computing, uncertainty management, and optimization approaches.

This book presents computational and statistical methods used by intelligent systems within the concept of Industry 4.0. The methods include among others evolution-based and swarm intelligence-based methods. Each method is explained in its fundamental aspects, while some notable bibliography is provided for further reading. This book describes each methods' principles and compares them. It is intended for researchers who are new in computational and statistical methods but also to experienced users.

META-HEURISTIC ALGORITHMS FOR ADVANCED DISTRIBUTED SYSTEMS Discover a collection of meta-heuristic algorithms for distributed systems in different application domains Meta-heuristic techniques are increasingly gaining favor as tools for optimizing distributed systems—generally, to enhance the utility and precision of database searches. Carefully applied, they can increase system effectiveness, streamline operations, and reduce cost. Since many of these techniques are derived from nature, they offer considerable scope for research and development, with the result that this field is growing rapidly. Meta-Heuristic Algorithms for Advanced Distributed Systems offers an overview of these techniques and their applications in various distributed systems. With strategies based on both global and local searching, it covers a wide range of key topics related to meta-heuristic algorithms. Those interested in the latest developments in distributed systems will find this book indispensable. Meta-Heuristic Algorithms for Advanced Distributed Systems readers will also find: Analysis of security issues, distributed system design, stochastic optimization techniques, and more Detailed discussion of meta-heuristic techniques such as the genetic algorithm, particle swam optimization, and many others Applications of optimized distribution systems in healthcare and other key??industries Meta-Heuristic Algorithms for Advanced Distributed Systems is ideal for academics and researchers studying distributed systems, their design, and their applications.

The current perspectives of smart and sustainable manufacturing systems hold important implications for current practices and understanding these concepts for further implications. This comprehensive reference text discusses both centralized and decentralized production systems, using variety of new cutting-edge approaches to solve the problem. The text covers simulation-based approaches including social network-based approaches, discrete event-based approaches, and knowledge based for smart and sustainable systems. It further covers mathematical models such as single-objective, multi-objective, and many-objective. The text discusses important topics including energy efficiency, transportation constrains for efficient and effective production, meta-heuristic and hybrid algorithms, and real-time monitoring and analysis for smart and sustainable production. This book- • Presents approaches to improve the objectives of sustain-ability and smart production systems. • Discusses Internet of Things (IoT) and Industrial Internet of Things (IIoT) concepts and its implementation for production systems. • Covers social network analysis method in distributed manufacturing systems. • Examines reckoning prognostics and diagnostics to monitor the health of the systems in perspective of distributed manufacturing. • Discusses aspects of Industry 4.0 in specific production systems. The text will be useful for graduate students and professional in the fields of mechanical engineering, production engineering, industrial engineering, and manufacturing.

This book has resulted from the activities of IFAC TC 5.2 “Manufacturing Modelling for Management and Control”. The book offers an introduction and advanced techniques of scheduling applications to cloud manufacturing and Industry 4.0 systems for larger audience. This book uncovers fundamental principles and recent developments in the theory and application of scheduling methodology to cloud manufacturing and Industry 4.0. The purpose of this book is to present recent developments in scheduling in cloud manufacturing and Industry 4.0 and to systemize these developments in new taxonomies and methodological principles to shape this new research domain. This book addresses the needs of both researchers and practitioners to uncover the challenges and opportunities of scheduling techniques’ applications to cloud manufacturing and Industry 4.0. For the first time, it comprehensively conceptualizes scheduling in cloud manufacturing and Industry 4.0 systems as a new research domain. The chapters of the book are written by the leading international experts and utilize methods of operations research, industrial engineering and computer science. Such a multi-disciplinary combination is unique and comprehensively deciphers major problem taxonomies, methodologies, and applications to scheduling in cloud manufacturing and Industry 4.0.

Industry 4.0 is changing how we manage operations to drive systems more intelligently. Technologies and applications are rapidly evolving. Disruptive technologies, such as artificial intelligence, big data, cloud computing and digital twin, are shaking up different industries and have motivated us to revisit engineering and management tools for improving system design, efficiency, effectiveness, reliability, and responsiveness. While these emerging technologies have powered new applications, novel industrial engineering methodologies are required to achieve the goals. Industrial Engineering was sprouted from major engineering disciplines that called for better professional understanding of industrialization. Ever since, the discipline of Industrial Engineering has been the star role player in confronting emerging industries; be it manufacturing, service, high tech products, outer space technology, information technology, industrial policy, ergonomics, and now the world’s greatest concern, sustainable development. This book presents the state-of-the-art in industrial engineering research from different countries and cities around the globe. The book covers a wide range of topics in industrial engineering, including: Demand Chain Management, E-business / Information Technology, Evolutionary Algorithm, Green Manufacturing/Management, Health Care Systems and more.

The book shows how simulation’s long history and close ties to industry since the third industrial revolution have led to its growing importance in Industry 4.0. The book emphasises the role of simulation in the new industrial revolution, and its application as a key aspect of making Industry 4.0 a reality – and thus achieving the complete digitisation of manufacturing and business. It presents various perspectives on simulation and demonstrates its applications, from augmented or virtual reality to process engineering, and from quantum computing to intelligent management. Simulation for Industry 4.0 is a guide and milestone for the simulation community, as well as those readers working to achieve the goals of Industry 4.0. The connections between simulation and Industry 4.0 drawn here will be of interest not only to beginners, but also to practitioners and researchers as a point of departure in the subject, and as a guide for new lines of study.