Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management

This text discusses prominent concepts and applications of AI technologies in the management of networked manufacturing enterprises. It includes the latest research in many areas and covers the role of emerging AI technologies.

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.

Aligns latest practice, innovation and case studies with academic frameworks and theories Includes most up-to-date research Includes supplementary material: sn.pub/extras

Autorentext

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.

Klappentext

Enterprise networks offer a wide range of new business opportunities, especially for small and medium-sized enterprises that are usually more flexible than larger companies. In order to be successful, however, performances and expected benefits have to be carefully evaluated and balanced: enterprises must ensure they become a member of the right network for the right task and must find an efficient, flexible, and sustainable working practice. A promising approach to finding such a practice is to combine analytical methods and knowledge-based approaches, in a distributed context.

Artificial intelligence (AI) techniques have been used to refine decision-making in networked enterprise processes, integrating people, information and products across the network boundaries. 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.

The Springer Series in Advanced Manufacturing publishes the best teaching and reference material to support students, educators andpractitioners in manufacturing technology and management. This international series includes advanced textbooks, research monographs, edited works and conference proceedings covering all subjects in advanced manufacturing. The series focuses on new topics of interest, new treatments of more traditional areas and coverage of the applications of information and communication technology (ICT) in manufacturing.

Inhalt
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.