A review of machine learning in dynamic scheduling of flexible manufacturing systems
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Publication:2744897
DOI10.1017/S0890060401153059zbMath0972.68544WikidataQ62038335 ScholiaQ62038335MaRDI QIDQ2744897
Javier Puente, David de la Fuente, Alberto Gomez, Paolo Priore
Publication date: 18 November 2001
Published in: Artificial Intelligence for Engineering Design, Analysis and Manufacturing (Search for Journal in Brave)
flexible manufacturing systemsmachine learningdiscrete simulationdynamic schedulingdispatching rules
Learning and adaptive systems in artificial intelligence (68T05) Deterministic scheduling theory in operations research (90B35) Computing methodologies and applications (68U99)
Related Items (7)
Dispatching rule selection with Gaussian processes ⋮ 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 ⋮ Pattern mining‐based pruning strategies in stochastic local searches for scheduling problems ⋮ A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times ⋮ Using data continualization and expansion to improve small data set learning accuracy for early flexible manufacturing system (FMS) scheduling ⋮ Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge
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