Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool

DOI10.1080/00207549208942903zbMath0825.90497OpenAlexW2002152339MaRDI QIDQ3990994

Shinichi Nakasuka, Taketoshi Yoshida

Publication date: 28 June 1992

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

Full work available at URL: https://doi.org/10.1080/00207549208942903

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Learning and adaptive systems in artificial intelligence (68T05) Deterministic scheduling theory in operations research (90B35) Production models (90B30)

Related Items (9)

Architecture of a dynamic production controller in CIM enterprise environments ⋮ Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge ⋮ Discovering dispatching rules using data mining ⋮ Using an unsupervized neural network and decision tree as knowledge acquisition tools for FMS scheduling ⋮ A learning-based methodology for dynamic scheduling in distributed manufacturing systems ⋮ DYNAMIC SCHEDULING OF MANUFACTURING SYSTEMS WITH MACHINE LEARNING ⋮ Using data continualization and expansion to improve small data set learning accuracy for early flexible manufacturing system (FMS) scheduling ⋮ Indentifying attributes for knowledge-based development in dynamic scheduling environments ⋮ Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge

Cites Work

• Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs
• A Review of Production Scheduling
• A Survey of Scheduling Rules

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