Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management

2010. Auflage 2010
ISBN: 978-1-84996-118-9
Verlag: Springer

Buch, Englisch, 508 Seiten, Format (B × H): 164 mm x 242 mm, Gewicht: 2020 g

Reihe: Springer Series in Advanced Manufacturing

Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management addresses prominent concepts and applications of AI technologies in the management of networked manufacturing enterprises.

The aim of this book is to align latest practices, innovation and case studies with academic frameworks and theories, where AI techniques are used efficiently for networked manufacturing enterprises. More specifically, it includes the latest research results and projects at different levels addressing quick-response system, theoretical performance analysis, performance and capability demonstration. The role of emerging AI technologies in the modelling, evaluation and optimisation of networked enterprises’ activities at different decision levels is also covered.

Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management is a valuable guide for postgraduates and researchers in industrial engineering, computer science, automation and operations research.

Zielgruppe

Research

Autoren/Hrsg.

Benyoucef, Lyes

Grabot, Bernard

Fachgebiete

• Mathematik | Informatik Mathematik Operations Research
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Computergestützte Fertigung

Inhaltsverzeichnis

Intelligent Manufacturing Systems.- Agent-based System for Knowledge Acquisition and Management Within a Networked Enterprise.- Multi-agent Simulation-based Decision Support System and Application in Networked Manufacturing Enterprises.- A Collaborative Decision-making Approach for Supply Chain Based on a Multi-agent System.- Web-services-based e-Collaborative Framework to Provide Production Control with Effective Outsourcing.- Isoarchic and Multi-criteria Control of Supply Chain Network.- Supply Chain Management Under Uncertainties: Lot-sizing and Scheduling Rules.- Meta-heuristics for Real-time Routing Selection in Flexible Manufacturing Systems.- Meta-heuristic Approaches for Multi-objective Simulation-based Optimization in Supply Chain Inventory Management.- Diverse Risk/Cost Balancing Strategies for Flexible Tool Management in a Supply Network.- Intelligent Integrated Maintenance Policies for Manufacturing Systems.- Enhancing the Effectiveness of Multi-pass Scheduling Through Optimization via Simulation.- Intelligent Techniques for Safety Stock Optimization in Networked Manufacturing Systems.- Real-world Service Interaction with Enterprise Systems in Dynamic Manufacturing Environments.- Factory of the Future: A Service-oriented System of Modular, Dynamic Reconfigurable and Collaborative Systems.- A Service-oriented Shop Floor to Support Collaboration in Manufacturing Networks.

Über Autor(innen)

Dr. Lyes Benyoucef received his PhD in Operations Research at the National Polytechnic Institute of Grenoble, France, in 2000 and his HDR (Research Director Thesis) degree from the University of Metz, France, in 2008. He is a senior researcher (CR1-HDR) at INRIA (the French National Institute for Research in Computer Science and Control). His main research interests include modelling and performance evaluation; and the simulation and optimization of supply chains and e-sourcing technologies.

Prof. Bernard Grabot teaches production management, industrial organization and ERP systems at the National Engineering School of Tarbes, France. He is a member of IFAC working groups on knowledge-based enterprise and editor-in-chief of the international journal, Engineering Applications of Artificial Intelligence. His main research interests concern the implementation of ERP systems, supply chain management and knowledge engineering.