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EnglishWe describe a novel approach for shop poor control combining a distributed multi-agent structure and computational intelligence techniques. Shop floor activities are controlled by a network of autonomous agents. Each agent makes its decision with a fuzzy algorithm evaluating all the alternative actions with multiple criteria based on real time measures of shop's conditions. A tuning mechanism of the decision algorithm allows agents to adapt themselves to the time varying operating conditions of the manufacturing system. The adaptation process follows a reinforcement learning schema. New agents are periodically created to replace the old ones according to the following strategy: the better the peformance of an agent in its life cycle, the higher the probability that new agents will inherit its decision rules. Preliminary experiments on a detailed simulation model of flexible assembling systems show the potentialities of the approach and suggest further improvements.
| Autori: | ||
| Titolo: | “Evolutionary learning agents for shop floor control” | |
| Data di pubblicazione: | 1999 | |
| Nome del convegno: | 7th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA'99 | |
| ISBN: | 0-7803-5670-5 | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/ETFA.1999.813086 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11589/15606
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