Benyoucef / Grabot Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management

1. Auflage 2010
ISBN: 978-1-84996-119-6
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 508 Seiten, eBook

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

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.

Leseproben

"Chapter 10 Diverse Risk/Cost Balancing Strategies for Flexible Tool Management in a Supply Network (p. 271-272)

D. D’Addona and R. Teti

Abstract This work is a part of a wider-scope research concerned with the development and implementation of a multi-agent tool management system (MATMS) for automatic tool procurement. The design, functioning, and performance of diverse flexible tool management strategies integrated in the MATMS is illustrated here. The MATMS operates in the frame of a negotiation based multiple-supplier network where a turbine blade producer (customer) requires from external tool manufacturers (suppliers) the performance of dressing operations on worn-out cubic boron nitride grinding wheels for nickel base alloy turbine blade fabrication. The diverse FTMS paradigms, configured as domain-specific problem-solving functions operating within the MATMS intelligent agent holding the responsibility for optimum tool inventory sizing and control, have been tested by tool inventory management simulations and comparison with real industrial cases. Keywords Tool management, inventory control, multi-agent systems, supply networks

10.1 Introduction

In recent times, novel software architecture to manage supply networks at the tactical and operational levels has emerged. The supply network is viewed as a system made of a set of intelligent (software) agents, each responsible for one or more activities in the supply network and each interacting with other agents in planning and executing their responsibilities (Fox et al., 2000).

The adoption of agent-based or multi-agent technology is founded on the three main system domain characteristics (Yuan et al., 2001): data, control, expertise or resources are inherently distributed; the system is naturally regarded as a society of autonomous cooperating components; the system contains legacy components that must interact with other, possibly new software components. Supply network management by its very nature has all the above domain characteristics (Sycara, 1998).

A supply network consists of suppliers, factories, warehouses, etc., working together to fabricate products and deliver them to customers. Parties involved in the supply network have their own resources, capabilities, tasks, and objectives. They cooperate with each other autonomously to serve common goals but also have their own interests.

A supply network is dynamic and involves the constant flows of information and materials across multiple functional areas both within and between network members. Multi-agent technology therefore appears to be particularly suitable to support collaboration in supply network management."

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.