Recommended Citation
Published in Proceedings of Rensselaer's Second International Conference on Computer Integrated Manufacturing: Troy, NY, May 21, 1990, pages 538-545.
NOTE: At the time of publication, the author Sema Alptekin was affiliated with the University of Missouri - Rolla. Currently, August 2008, she is Professor of Industrial & Manufacturing Engineering and Director of the University Honors Program at California Polytechnic State University - San Luis Obispo .
The definitive version is available at https://doi.org/10.1109/CIM.1990.128158.
Abstract
An adaptive expert scheduler was developed that learns by itself and adapts to the dynamic FMS environment. This hybrid system uses a symbiotic architecture composed of expert systems (ESs) and artificial neural networks (ANNs) 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.
Disciplines
Industrial Engineering | Manufacturing
Copyright
1990 IEEE.
Publisher statement
Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
URL: https://digitalcommons.calpoly.edu/ime_fac/9