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Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling

Ghasemi, Amir; Kabak, Kamil Erkan; Heavey, Cathal; Feng, B.; Pedrielli, G.; Peng, Y.; Shashaani, S.; Song, E.; Corlu, C.G.; Lee, L.H.; Chew, E.P.; Roeder, T.; Lendermann, P.

Industry 4.0 has placed an emphasis on real-time decision making in the execution of systems, such as semiconductor manufacturing. This article will evaluate a scheduling methodology called Evolutionary Learning Based Simulation Optimization (ELBSO) using data generated by a Manufacturing Execution System …

2023-03-02AlgemeenConferentiebijdrage

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Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling

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