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 This contributed volume presents  state-of-the-art advances in logistics theory in various fields as well as case studies. The book reports on a number of recently conducted studies in the Dinalog and the EffizienzCluster LogistikRuhr, thus bridging the gap between different perspectives of theoretical and applied research. A selection of theoretical topics, practical examples, case studies and project reports  is presented in this volume. The editors carefully selected contributions from a wide variety of projects, which were carried out in both the Dinalog cluster and the Effizienzcluster LogistikRuhr. The contributions are grouped in five main sections, each representing key domains in the evolution of logistics and supply chain management: sustainability, urban logistics, value chain management, IT-based innovation, knowledge management. This book is intended for both researchers and practitioners in the field of logistics and supply chain management, to serve as an important source of information for further research as well as to stimulate further innovation.

Civil Engineering --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Business logistics. --- Supply chain management --- Industrial management --- Logistics --- Engineering economy. --- Production management. --- Information Systems. --- Engineering Economics, Organization, Logistics, Marketing. --- Operations Management. --- Management of Computing and Information Systems. --- Manufacturing management --- Economy, Engineering --- Engineering economics --- Industrial engineering --- Engineering economics. --- Management information systems. --- Computer science. --- Informatics --- Science --- Computer-based information systems --- EIS (Information systems) --- Executive information systems --- MIS (Information systems) --- Sociotechnical systems --- Information resources management --- Management --- Communication systems --- Industrial Management. --- Electronic data processing --- IT Operations. --- Management. --- Business administration --- Business enterprises --- Business management --- Corporate management --- Corporations --- Industrial administration --- Management, Industrial --- Rationalization of industry --- Scientific management --- Business --- Industrial organization

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Materialflusssysteme als Symbiose aus betrieblicher Organisation und physischer, fördertechnischer Umsetzung stehen im Mittelpunkt dieses Grundlagenwerks der Intralogistik. Zahlreiche Tabellen, Grafiken und technische Daten sowie eine Fülle von Systembeispielen und Auswahlmöglichkeiten liefern dem Leser einen vollständigen Überblick über die wesentlichen Komponenten der Förder- und Lagertechnik. Die Gestaltung effizienter Materialflusssysteme setzt Kenntnisse über den Stand der heute verfügbaren Technologien für die vielfältigen Materialflussoperationen und deren Wechselwirkungen in den zahlreichen Anwendungen voraus. Beim Abwägen der großen Auswahl an Gestaltungsmöglichkeiten, die aus den vielen Technologien hervorgeht, steht dieses Buch sowohl Studierenden als auch Praktikern, Planern und Entscheidern zur Seite. Eine Einführung in die Planung und analytische Berechnung von Materialflusssystemen vervollständigt den grundlegenden Überblick über die Komponenten zur Erfüllung der zentralen logistischen Funktionen, Dinge zu lagern, zu transportieren, zusammenzuführen und zu verteilen.

Education. --- Engineering economics. --- Engineering economy. --- Control engineering. --- Robotics. --- Mechatronics. --- Production management. --- Management. --- Education, general. --- Engineering Economics, Organization, Logistics, Marketing. --- Control, Robotics, Mechatronics. --- Operations Management. --- Management.

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Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 3 describes how the resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how machine learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from machine learning, for example by uncovering hidden characteristics of the wireless channel.

SCIENCE / Chemistry / General. --- Artificial Intelligence. --- Big Data and Machine Learning. --- Cyber-physical systems. --- Data mining for Ubiquitous System Software. --- Embedded Systems and Machine Learning. --- Highly Distributed Data. --- ML on Small devices. --- Machine learning for knowledge discovery. --- Machine learning in high-energy physics. --- Resource-Aware Machine Learning. --- Resource-Constrained Data Analysis.