Leveraging Synthetic Data and Machine Learning for Shared Facility Scheduling

Paper

Rabaev, Marsel, Pratama, Handy and Chan, Ka C.. 2024. "Leveraging Synthetic Data and Machine Learning for Shared Facility Scheduling." 17th International Conference on Information Technology and Applications (ICITA 2023). Turin, Italy 20 - 21 Oct 2023 Singapore. Springer. https://doi.org/10.1007/978-981-99-8324-7_34
Paper/Presentation Title

Leveraging Synthetic Data and Machine Learning for Shared Facility Scheduling

Presentation TypePaper
AuthorsRabaev, Marsel, Pratama, Handy and Chan, Ka C.
Journal or Proceedings TitleProceedings of 17th International Conference on Information Technology and Applications (ICITA 2023)
Journal Citation839, pp. 401-410
Number of Pages10
Year2024
PublisherSpringer
Place of PublicationSingapore
ISBN9789819983230
9789819983247
Digital Object Identifier (DOI)https://doi.org/10.1007/978-981-99-8324-7_34
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-981-99-8324-7_34
Web Address (URL) of Conference Proceedingshttps://link.springer.com/book/10.1007/978-981-99-8324-7
Conference/Event17th International Conference on Information Technology and Applications (ICITA 2023)
Event Details
17th International Conference on Information Technology and Applications (ICITA 2023)
Parent
International Conference on Information Technology and Applications
Delivery
In person
Event Date
20 to end of 21 Oct 2023
Event Location
Turin, Italy
AbstractThis research explores the applicability of machine learning (ML) algorithms in addressing key challenges in manufacturing planning and control (MPC), with a specific focus on capacity requirement planning (CRP) and scheduling. To effectively train ML algorithms, a discrete-event simulation (DES) methodology is employed to construct a system model, generating synthetic data through simulations across diverse scenarios. The proposed framework’s efficacy is empirically evaluated through three distinct case studies, involving sequential, parallel, and shared facility layouts. The sequential and parallel layouts assess overall feasibility and capacity requirement planning, while the shared facility layout investigates scheduling within a more complex flexible manufacturing system. The research findings provide compelling evidence supporting the utilization of synthetic data for training ML models, facilitating efficient resolution of facility scheduling challenges in manufacturing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
KeywordsDiscrete-event simulation; Manufacturing planning and control; synthetic data; system modeling; facility scheduling; ma-chine learning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460501. Data engineering and data science
Public Notes

The accessible file is the accepted version of the paper. Please refer to the URL for the published version.

SeriesLecture Notes in Networks and Systems
Byline AffiliationsUniversity of New South Wales
University of Southern Queensland
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