This book deals with complex problems in the fields of logistics and supply chain management and discusses advanced methods, especially from the field of computational intelligence (CI), for solving them. The first two chapters provide general introductions to logistics and supply chain management on the one hand, and to computational intelligence on the other hand. The subsequent chapters cover specific fields in logistics and supply chain management, work out the most relevant problems found in those fields, and discuss approaches for solving them. Chapter 3 discusses problems in the field of production and inventory management. Chapter 4 considers planning activities on a finer level of granularity which is usually denoted as scheduling. In chapter 5 problems in transportation planning such as different types of vehicle routing problems are considered. While chapters 3 to 5 rather discuss planning problems which appear on an operative level, chapter 6 discusses the strategic problem of designing a supply chain or network. The final chapter provides an overview of academic and commercial software and information systems for the discussed applications. There appears to be a gap between general textbooks on logistics and supply chain management and more specialized literature dealing with methods for computational intelligence, operations research, etc., for solving the complex operational problems in these fields. For readers, it is often difficult to proceed from introductory texts on logistics and supply chain management to the sophisticated literature which deals with the usage of advanced methods. This book fills this gap by providing state-of-the-art descriptions of the corresponding problems and suitable methods for solving them.

Logistics and supply chain management deal with managing the ?ow of goods or services within a company, from suppliers to customers, and along a supply chain where companies act as suppliers as well as customers. As transportation is at the heart of logistics, the design of tra?c and transportation networks combined with the routing of vehicles and goods on the networks are important and demanding planning tasks. The in?uence of transport, logistics, and s- ply chain management on the modern economy and society has been growing steadily over the last few decades. The worldwide division of labor, the conn- tion of distributed production centers, and the increased mobility of individuals lead to an increased demand for e?cient solutions to logistics and supply chain management problems. On the company level, e?cient and e?ective logistics and supply chain management are of critical importance for a company’s s- cessanditscompetitiveadvantage. Properperformanceofthelogisticsfunctions can contribute both to lower costs and to enhanced customer service. Computational Intelligence (CI) describes a set of methods and tools that often mimic biological or physical principles to solve problems that have been di?cult to solve by classical mathematics. CI embodies neural networks, fuzzy logic, evolutionary computation, local search, and machine learning approaches. Researchersthat workinthis areaoften comefromcomputer science,operations research,or mathematics, as well as from many di?erent engineering disciplines. Popular and successful CI methods for optimization and planning problems are heuristic optimization approaches such as evolutionary algorithms, local search methods, and other types of guided search methods.

Sustainable supply chain management involves integrating environmentally and financially viable practices into the complete supply chain lifecycle, from product design and development to material selection and sourcing, manufacturing, packaging, transportation, and distribution. A sustainable supply chain ensures balance between economic, social, and environmental performances – such as better assurance of human rights, ethical work practices, carbon footprint reduction, waste management, and resource efficiency. Computational Intelligence Techniques for Sustainable Supply Chain Management presents state-of-the-art computational intelligence techniques and applications for supply chain sustainability issues and logistic problems, filling the gap between general textbooks on sustainable supply chain management and more specialized literature dealing with methods for computational intelligence. This book focuses on addressing problems in advanced topics in the sustainable supply chain, and will appeal to practitioners, managers, researchers, academicians, students, and professionals interested in sustainable logistics, sustainable procurement, sustainable manufacturing, sustainable inventory and production management, sustainable scheduling, sustainable transportation, and sustainable network design. Serves as a reference on computational intelligence–enabled sustainable supply chains for graduate students in computer/data science, industrial engineering, industrial ecology, and business Explores key topics in sustainable supply chain informatics, that is, heuristics, metaheuristics, robotics, simulation, machine learning, big data analytics and artificial intelligence Provides a foundation for industry leaders and professionals to understand recent and cutting-edge methodologies and technologies in the domain of sustainable supply chain powered by computational intelligence techniques

Computational Intelligence (CI) is a term corresponding to a new generation of algorithmic methodologies in artificial intelligence, which combines elements of learning, adaptation, evolution and approximate (fuzzy) reasoning to create programs that can be considered intelligent. Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues. Emphasis is given to techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research.

Master's Thesis from the year 2020 in the subject Business economics - Supply, Production, Logistics, , language: English, abstract: Industrialisation has greatly changed people's lives since the eighteenth century. At that time, the focus of activities shifted from the physical to the cognitive. We are now in the fourth industrial revolution. Through the development of ever better artificial systems, more and more cognitive activities are being carried out by machines. But what exactly does artificial intelligence mean? What changes has it already created in supply chain management and what is still possible in the future? What are the risks associated with advancing digitalisation? In his publication, Johannes Hangl creates a comprehensive picture of the status of current and future developments as well as trends in the use of artificial intelligence. He shows what possibilities, effects, opportunities and risks it has for supply chain management. From the contents: - AI; - Internet of Things; - Logistics; - Cyber-Physical Systems

ARTIFICIAL INTELLIGENT TECHNIQUES FOR WIRELESS COMMUNICATION AND NETWORKING The 20 chapters address AI principles and techniques used in wireless communication and networking and outline their benefit, function, and future role in the field. Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments. Audience Researchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies.