We 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.

“Evolutionary learning agents for shop floor control”

NASO, David
1999

Abstract

We 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.
7th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA'99
0-7803-5670-5
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/15606
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact