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

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@InProceedings{Ghasemi:2022:WSC,

• author = "Amir Ghasemi and Kamil Erkan Kabak and Cathal Heavey",
• booktitle = "2022 Winter Simulation Conference (WSC)",
• title = "Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling",
• year = "2022",
• pages = "3406--3417",
• 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 frontend fab to train and test. The results were statistically evaluated for the quality of the fit generated by the meta-model.",
• keywords = "genetic algorithms, genetic programming, Job shop scheduling, Decision making, Metamodelling, Machine learning, Semiconductor device manufacture, Real-time systems, Data models",
• DOI = "10.1109/WSC57314.2022.10015436",
• ISSN = "1558-4305",
• month = dec,
• notes = "Also known as \cite{10015436}",

}

Genetic Programming entries for Amir Ghasemi Kamil Erkan Kabak Cathal Heavey

Citations