Using data continualization and expansion to improve small data set learning accuracy for early flexible manufacturing system (FMS) scheduling

DOI10.1080/00207540600559849zbMath1114.90383OpenAlexW2165100383MaRDI QIDQ3430239

Der-Chiang Li, Chih-Sen Wu, Fengming M. Chang

Publication date: 21 March 2007

Published in: International Journal of Production Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207540600559849

zbMATH Keywords

schedulingmachine learningflexible manufacturing systemsmall data set

Mathematics Subject Classification ID

Deterministic scheduling theory in operations research (90B35) Production models (90B30)

Cites Work

• Principle of information diffusion
• A diffusion-neural-network for learning from small samples
• Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge
• A review of machine learning in dynamic scheduling of flexible manufacturing systems
• Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool
• Indentifying attributes for knowledge-based development in dynamic scheduling environments
• An intelligent controller for manufacturing cells
• Aframework fora goal-drivenapproachto grouptechnology applications using conceptual clustering
• Simulation metamodelling with neural networks: An experimental investigation

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