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Titolo:
A LEARNING-BASED METHODOLOGY FOR DYNAMIC SCHEDULING IN DISTRIBUTED MANUFACTURING SYSTEMS
Autore:
CHIU C; YIH Y;
Indirizzi:
PURDUE UNIV,SCH IND ENGN,1287 GRISSOM HALL W LAFAYETTE IN 47907 PURDUE UNIV,SCH IND ENGN W LAFAYETTE IN 47907
Titolo Testata:
International Journal of Production Research
fascicolo: 11, volume: 33, anno: 1995,
pagine: 3217 - 3232
SICI:
0020-7543(1995)33:11<3217:ALMFDS>2.0.ZU;2-R
Fonte:
ISI
Lingua:
ENG
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
NO
Recensione:
Indirizzi per estratti:
Citazione:
C. Chiu e Y. Yih, "A LEARNING-BASED METHODOLOGY FOR DYNAMIC SCHEDULING IN DISTRIBUTED MANUFACTURING SYSTEMS", International Journal of Production Research, 33(11), 1995, pp. 3217-3232

Abstract

To enhance productivity in a distributed manufacturing system under heterarchical control, we develop a framework of dynamic scheduling scheme that explores routeing flexibility and handles uncertainties. We propose a learning-based methodology to extract scheduling knowledge for dispatching parts to machines. The proposed methodology includes three modules: discrete-event simulation, instance generation, and incremental induction. First, a sophisticated simulation module is developedto implement a dynamic scheduling scheme, to generate training examples, and to evaluate the methodology. Second, the search for training examples (good schedules) is successfully fulfilled by the genetic algorithm. Finally, we propose a tolerance-based learning algorithm that does not only acquire general scheduling rules from the training examples, but also adapts to any newly observed examples and thus facilitates knowledge modification. The experimental results show that the dynamic scheduling scheme significantly outperforms the static scheduling scheme with a single dispatching rule in a distributed manufacturing system.

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Documento generato il 27/11/20 alle ore 06:36:34