Name
Abstract | ||
|---|---|---|
Current research in multi-agent heterarchical control for holonic systems is usually focused in real-time scheduling algorithms, where agents explore the routing or process sequencing flexibility in real-time. In this paper we investigate the impact of the dynamic job routing and job sequencing decisions on the overall optimization of the system's performance. An approach to the optimization of local decisions to assure global optimization is developed within the framework of a Neural Collective Intelligence (NECOIN). Reinforcement learning (RL) algorithms are used at the local level, while generalization of Q-neural algorithm is used to optimize the global behaviour. A simulation test bed for the evaluation of such types of multi-agent control architectures for holonic manufacturing systems integrating discrete-event simulation facilities is implemented over JADE agent platform. Performance results of the simulation experiments are presented and discussed. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/0-387-22829-2_19 | INTERNATIONAL FEDERATION FOR INFORMATION PROCESSING |
Keywords | Field | DocType |
collective intelligence,reinforcement learning,discrete event simulation,real time,simulation experiment,test bed,global optimization | Global optimization,Collective intelligence,Scheduling (computing),Manufacturing systems,Real-time computing,Communication diagram,Optimization algorithm,Engineering,Reinforcement learning,Distributed computing | Conference |
Volume | ISSN | Citations |
159 | 1571-5736 | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leonid Sheremetov | 1 | 198 | 28.37 |
| Luis Mateus Rocha | 2 | 333 | 36.91 |
| Juan Guerra | 3 | 4 | 0.77 |
| Jorge Martínez Muñoz | 4 | 6 | 1.13 |