Centre for Economic Transformation| CET

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

Conference

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.

Reference Ghasemi, A., Kabak, K. E., & Heavey, C. (2023). Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling. In B. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C. G. Corlu, L. H. Lee, E. P. Chew, T. Roeder, & P. Lendermann (Eds.), Proceedings of the 2022 Winter Simulation Conference (pp. 3406-3417). (Proceedings - Winter Simulation Conference ). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC57314.2022.10015436
Published by  Centre for Economic Transformation 2 March 2023

Publication date

Mar 2023

Author(s)

Amir Ghasemi
Kamil Erkan Kabak
Cathal Heavey

Publications:

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