@inproceedings{4aba535180e049e7a37afdae78ab67a0,
title = "Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling",
abstract = "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.",
keywords = "Job shop scheduling, Decision making, Metamodeling, Machine learning, Semiconductor device manufacture, Real-time systems, Data models",
author = "Amir Ghasemi and Kabak, {Kamil Erkan} and Cathal Heavey",
year = "2023",
month = mar,
day = "2",
doi = "10.1109/WSC57314.2022.10015436",
language = "English",
isbn = "9781665476621",
series = "Proceedings - Winter Simulation Conference ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3406--3417",
editor = "B. Feng and Pedrielli, {G. } and Y. Peng and S. Shashaani and E. Song and C.G. Corlu and L.H. Lee and E.P. Chew and T. Roeder and P. Lendermann",
booktitle = "Proceedings of the 2022 Winter Simulation Conference",
address = "United States",
note = "Winter Simulation Conference 2022 : Reimagine Tomorrow ; Conference date: 11-12-2022 Through 14-12-2022",
}