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

Amir Ghasemi; Kamil Erkan Kabak; Cathal Heavey

doi:10.1109/WSC57314.2022.10015436

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

Amir Ghasemi, Kamil Erkan Kabak, Cathal Heavey

Faculty of Business and Economics (FBE)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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	https://doi.org/10.1109/WSC57314.2022.10015436
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.

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10.1109/WSC57314.2022.10015436

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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

Ghasemi, Amir ; Kabak, Kamil Erkan ; Heavey, Cathal. / Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling. Proceedings of the 2022 Winter Simulation Conference. editor / B. Feng ; G. Pedrielli ; Y. Peng ; S. Shashaani ; E. Song ; C.G. Corlu ; L.H. Lee ; E.P. Chew ; T. Roeder ; P. Lendermann. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 3406-3417 (Proceedings - Winter Simulation Conference ).

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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",

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doi = "10.1109/WSC57314.2022.10015436",

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note = "Winter Simulation Conference 2022 : Reimagine Tomorrow ; Conference date: 11-12-2022 Through 14-12-2022",

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Ghasemi, A, Kabak, KE & 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, CG Corlu, LH Lee, EP Chew, T Roeder & P Lendermann (eds), Proceedings of the 2022 Winter Simulation Conference. Proceedings - Winter Simulation Conference , Institute of Electrical and Electronics Engineers Inc., pp. 3406-3417, Winter Simulation Conference 2022, Singapore, Singapore, 11/12/22. https://doi.org/10.1109/WSC57314.2022.10015436

Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling. / Ghasemi, Amir; Kabak, Kamil Erkan; Heavey, Cathal.
Proceedings of the 2022 Winter Simulation Conference. ed. / B. Feng; G. Pedrielli; Y. Peng; S. Shashaani; E. Song; C.G. Corlu; L.H. Lee; E.P. Chew; T. Roeder; P. Lendermann. Institute of Electrical and Electronics Engineers Inc., 2023. p. 3406-3417 (Proceedings - Winter Simulation Conference ).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling

AU - Ghasemi, Amir

AU - Kabak, Kamil Erkan

AU - Heavey, Cathal

PY - 2023/3/2

Y1 - 2023/3/2

N2 - 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.

AB - 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.

KW - Job shop scheduling

KW - Decision making

KW - Metamodeling

KW - Machine learning

KW - Semiconductor device manufacture

KW - Real-time systems

KW - Data models

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SN - 9781665476621

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SP - 3406

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BT - Proceedings of the 2022 Winter Simulation Conference

A2 - Feng, B.

A2 - Pedrielli, G.

A2 - Peng, Y.

A2 - Shashaani, S.

A2 - Song, E.

A2 - Corlu, C.G.

A2 - Lee, L.H.

A2 - Chew, E.P.

A2 - Roeder, T.

A2 - Lendermann, P.

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T2 - Winter Simulation Conference 2022

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Ghasemi A, Kabak KE, Heavey C. Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling. In Feng B, Pedrielli G, Peng Y, Shashaani S, Song E, Corlu CG, Lee LH, Chew EP, Roeder T, Lendermann P, editors, Proceedings of the 2022 Winter Simulation Conference. Institute of Electrical and Electronics Engineers Inc. 2023. p. 3406-3417. (Proceedings - Winter Simulation Conference ). doi: 10.1109/WSC57314.2022.10015436

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

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