Browsing by Author "Kabak, Kamil Erkan"

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    Demonstration of the Feasibility of Real Time Application of Machine Learning To Production Scheduling
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ghasemi A.; Kabak K.E.; Heavey C.; Ghasemi, Amir; Heavey, Cathal; Kabak, Kamil Erkan
    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 (MES) for scheduling a Stochastic Job Shop Scheduling Problem (SJSSP). ELBSO is embedded within Ordinal Optimization (OO), where in the first phase it uses a meta model, which previously was trained by a Discrete Event Simulation model of a SJSSP. The meta model used within ELBSO uses Genetic Programming (GP)-based Machine Learning (ML). Therefore, instead of using the DES model to train and test the meta model, this article uses historical data from a front-end fab to train and test. The results were statistically evaluated for the quality of the fit generated by the meta-model. © 2022 IEEE.