A strategic framework for networked manufacturing
Computers in Industry - Special issue on advances in computer integrated production in honour of professor C.L. Moodie's retirement
The Design of Intelligent Agents: A Layered Approach
The Design of Intelligent Agents: A Layered Approach
Social knowledge in multi-agent systems
Mutli-agents systems and applications
Multiagent Systems for Manufacturing Control
Multiagent Systems for Manufacturing Control
ExPlanTech: Multiagent Support for Manufacturing Decision Making
IEEE Intelligent Systems
Multi-site coordination using a multi-agent system
Computers in Industry
Multi-plant production scheduling in SMEs
Robotics and Computer-Integrated Manufacturing
Multi-behavior agent model for planning in supply chains: An application to the lumber industry
Robotics and Computer-Integrated Manufacturing
Reconfiguration framework of a supply network based on flexibility strategies
Computers and Industrial Engineering
Simulation of cross-border competitions of free Internet content providers
Computers in Industry
Organizational simulation in support of global manufacturing enterprises
Information-Knowledge-Systems Management - Enterprise Transformation: Manufacturing in a Global Enterprise
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In today's industrial context, competitiveness is closely associated to supply chain performance. Coordination between business units is essential to increase this performance, in order to produce and deliver products on time to customers, at a competitive price. While planning systems usually follow a single straightforward production planning process, this paper proposes that partners adapt together their local planning process (i.e. planning behaviours) to the different situations met in the supply chain environment. Because each partner can choose different behaviour and all behaviours will have an impact on the overall performance, it is difficult to know which is preferable for each partner to increase their performance. Using agent-based technology, simulation experiments have been undertaken to verify if multi-behaviour planning agents who can change planning behaviours to adapt to their environment can increase supply chain performance. These agents have been implemented in an agent-based planning platform, using a case study illustrating a lumber supply chain. The performance analysis shows that advanced planning systems can take advantage of using multiple planning processes, because of the dynamic context of supply chains.