DocumentCode :
2744632
Title :
Adaptive scheduling and control using artificial neural networks and expert systems for a hierarchical/distributed FMS architecture
Author :
Rabelo, Luis Carlos ; Alptekin, Sema
Author_Institution :
Dept. of Eng. Manage., Missouri Univ., Rolla, MO, USA
fYear :
1990
fDate :
21-23 May 1990
Firstpage :
538
Lastpage :
545
Abstract :
An adaptive expert scheduler that learns by itself and adapts to the dynamic FMS (flexible manufacturing system) environment was developed. This hybrid system uses a symbiotic architecture composed of expert systems and artificial neural networks and provides a learning scheme guided by past experience. The artificial neural networks recognize patterns in the tasks to be solved in order to select the best scheduling rule according to different criteria. The expert systems, on the other hand, drive the inference strategy and interpret the constraints and restrictions imposed by the upper levels of the control hierarchy of the flexible manufacturing system. The level of self-organization achieved provides a system with a higher probability of success than traditional approaches
Keywords :
adaptive control; expert systems; flexible manufacturing systems; learning systems; neural nets; scheduling; FMS architecture; adaptive control; expert systems; flexible manufacturing system; inference strategy; learning scheme; neural networks; scheduling; self-organization; symbiotic architecture; Adaptive scheduling; Adaptive systems; Artificial neural networks; Control systems; Dynamic scheduling; Expert systems; Flexible manufacturing systems; Job shop scheduling; Pattern recognition; Symbiosis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Integrated Manufacturing, 1990., Proceedings of Rensselaer's Second International Conference on
Conference_Location :
Troy, NY
Print_ISBN :
0-8186-1966-X
Type :
conf
DOI :
10.1109/CIM.1990.128158
Filename :
128158
Link To Document :
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