Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach
DOI10.1080/00207540701846236zbMath1198.90066OpenAlexW2035057469MaRDI QIDQ3055418
Publication date: 7 November 2010
Published in: International Journal of Production Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207540701846236
CIMdata miningneural network applicationsautomated manufacturing systemsFMS controlmanufacturing control systemsshop floor controlsemiconductor manufacturee-manufacturing
Learning and adaptive systems in artificial intelligence (68T05) Transportation, logistics and supply chain management (90B06) Stochastic network models in operations research (90B15)
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- The impact of preprocessing on data mining: an evaluation of classifier sensitivity in direct marketing
- Job shop scheduling with a genetic algorithm and machine learning
- A study of scheduling rules of flexible manufacturing systems: A simulation approach
- Integration of inductive learning and neural networks for multi-objective FMS scheduling
- A learning-based methodology for dynamic scheduling in distributed manufacturing systems
- Operating an FMC by a decision-tree-based adaptive production control system
- Auto-bias selection for developing learning-based scheduling systems
- A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms
- Data mining. Concepts and techniques
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