Using granular computing model to induce scheduling knowledge in dynamic manufacturing environments

International Journal of Computer Integrated Manufacturing

Volumn 21, Issue 5, 2008, Pages 569-583

  Chen, L S ^a   Su, C T ^b  

^a CHAOYANG UNIVERSITY OF TECHNOLOGY   (Taiwan)

^b NATIONAL TSING HUA UNIVERSITY   (Taiwan)

Author keywords

Class imbalance problems; Data mining; Granular computing; Neural networks; Support vector machines

Indexed keywords

DATA ACQUISITION; DECISION MAKING; DECISION THEORY; DECISION TREES; DYNAMIC MODELS; EDUCATION; FLEXIBLE MANUFACTURING SYSTEMS; KNOWLEDGE ACQUISITION; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; MANUFACTURE; SCHEDULING;

CLUSTER-BASED; DYNAMIC MANUFACTURING ENVIRONMENTS; EXPERIMENTAL RESULTS; FLEXIBLE MANUFACTURING SYSTEM (FMS); GRANULAR COMPUTING (GRC); IMBALANCED DATA; INDUCTIVE LEARNING; LOW FREQUENCY (LF); POOR PERFORMANCE; PREDICTIVE ABILITIES; SAMPLING TECHNIQUES;

LEARNING ALGORITHMS;

EID: 46249123473     PISSN: 0951192X     EISSN: 13623052     Source Type: Journal    
DOI: 10.1080/09511920701381255     Document Type: Article

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