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

School of Engineering

Amsterdam University of Applied Sciences
Izmir Ekonomi University

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

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, WSC 2022
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
Number of pages	12
ISBN (Electronic)	9798350309713
DOIs	https://doi.org/10.1109/WSC57314.2022.10015436
Publication status	Published - 2022
Event	2022 Winter Simulation Conference, WSC 2022 - Guilin, China Duration: 11 Dec 2022 → 14 Dec 2022

Publication series

Name	Proceedings - Winter Simulation Conference
Volume	2022-December
ISSN (Print)	0891-7736

Conference

Conference	2022 Winter Simulation Conference, WSC 2022
Country/Territory	China
City	Guilin
Period	11/12/22 → 14/12/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1109/WSC57314.2022.10015436

Cite this

Ghasemi, A., Kabak, K. E., & Heavey, C. (2022). 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, WSC 2022 (pp. 3406-3417). (Proceedings - Winter Simulation Conference; Vol. 2022-December). 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, WSC 2022. 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., 2022. pp. 3406-3417 (Proceedings - Winter Simulation Conference).

@inproceedings{749f8ea6f501425cb374352f8332a63d,

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.",

author = "Amir Ghasemi and Kabak, \{Kamil Erkan\} and Cathal Heavey",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 Winter Simulation Conference, WSC 2022 ; Conference date: 11-12-2022 Through 14-12-2022",

year = "2022",

doi = "10.1109/WSC57314.2022.10015436",

language = "English",

series = "Proceedings - Winter Simulation Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3406--3417",

editor = "B. Feng and G. Pedrielli 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, WSC 2022",

}

Ghasemi, A, Kabak, KE & Heavey, C 2022, 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, WSC 2022. Proceedings - Winter Simulation Conference, vol. 2022-December, Institute of Electrical and Electronics Engineers Inc., pp. 3406-3417, 2022 Winter Simulation Conference, WSC 2022, Guilin, China, 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, WSC 2022. 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., 2022. p. 3406-3417 (Proceedings - Winter Simulation Conference; Vol. 2022-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › 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 - 2022

Y1 - 2022

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.

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U2 - 10.1109/WSC57314.2022.10015436

DO - 10.1109/WSC57314.2022.10015436

M3 - Conference contribution

AN - SCOPUS:85147456582

T3 - Proceedings - Winter Simulation Conference

SP - 3406

EP - 3417

BT - Proceedings of the 2022 Winter Simulation Conference, WSC 2022

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.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 Winter Simulation Conference, WSC 2022

Y2 - 11 December 2022 through 14 December 2022

ER -

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, WSC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. 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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