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.
Original language | English |
---|---|
Title of host publication | Proceedings of the 2022 Winter Simulation Conference |
Editors | B. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, P. Lendermann |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3406-3417 |
ISBN (Electronic) | 9781665476614 |
ISBN (Print) | 9781665476621 |
DOIs | |
Publication status | Published - 2 Mar 2023 |
Event | Winter Simulation Conference 2022: Reimagine Tomorrow - Singapore, Singapore Duration: 11 Dec 2022 → 14 Dec 2022 |
Publication series
Name | Proceedings - Winter Simulation Conference |
---|---|
ISSN (Print) | 0891-7736 |
ISSN (Electronic) | 1558-4305 |
Conference
Conference | Winter Simulation Conference 2022 |
---|---|
Country/Territory | Singapore |
City | Singapore |
Period | 11/12/22 → 14/12/22 |
Funding
This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI 16/RC/3918, co-funded by the European Regional Development Fund.