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

Amir Ghasemi, Kamil Erkan Kabak, Cathal Heavey

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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.
Original languageEnglish
Title of host publicationProceedings of the 2022 Winter Simulation Conference
EditorsB. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, P. Lendermann
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665476614
ISBN (Print)9781665476621
Publication statusPublished - 2 Mar 2023
EventWinter Simulation Conference 2022: Reimagine Tomorrow - Singapore, Singapore
Duration: 11 Dec 202214 Dec 2022

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736
ISSN (Electronic)1558-4305


ConferenceWinter Simulation Conference 2022


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