Artificial Life
An experimental benchmarking of two multi-agent architectures for production scheduling and control
Computers in Industry - Special issue on intelligent manufacturing systems
Reinforcement Learning
A soft computing approach for task contracting in multi-agent manufacturing control
Computers in Industry - Special issue: Soft computing in industrial applications
Multi-agent coordination and control using stigmergy
Computers in Industry
Ant Colony Optimization
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Semantic Extension of Agent-Based Control: The Packing Cell Case Study
HoloMAS '09 Proceedings of the 4th International Conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing
Distributed control of production systems
Engineering Applications of Artificial Intelligence
Semi-heterarchical control of FMS: From theory to application
Engineering Applications of Artificial Intelligence
Personal Rapid Transit in an open-control framework
Computers and Industrial Engineering
New trends of visualization in smart production control systems
HoloMAS'11 Proceedings of the 5th international conference on Industrial applications of holonic and multi-agent systems for manufacturing
Bio-inspired multi-agent systems for reconfigurable manufacturing systems
Engineering Applications of Artificial Intelligence
Heterarchical production control in manufacturing systems using the potential fields concept
Journal of Intelligent Manufacturing
Handling disruptions in manufacturing systems: An immune perspective
Engineering Applications of Artificial Intelligence
Journal of Intelligent Manufacturing
The control of myopic behavior in semi-heterarchical production systems: A holonic framework
Engineering Applications of Artificial Intelligence
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This paper illustrates the capacity of a stigmergic routing control model to automatically find efficient routing paths for active products in flexible manufacturing systems (FMSs) undergoing perturbations. The proposed model is based upon a functional architecture with two levels: a virtual level in which virtual active products (VAPs) evolve stochastically in accelerated time, and a physical level in which physical active products (PAPs) evolve deterministically in real-time. The physical active products follow the best paths that have been detected on the virtual level, with a virtual level exploration being triggered when a perturbation is diagnosed in the transportation system. The data used for the simulation on the virtual level is then updated to reflect the real state of the transportation system. The model's adaptive capabilities are illustrated with several simulation scenarios using NetLogo software, and an on-going real implementation is presented.