Scheduling of Manufacturing Systems with AI
Research Seminar Series
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Date
26 Nov 2024
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Organiser
Department of Industrial and Systems Engineering, PolyU
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Time
13:00 - 14:30
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Venue
Online via ZOOM
Speaker
Dr Hyun-Jung Kim
Remarks
Meeting link will be sent to successful registrants
Summary
This talk will present real-life industrial scheduling problems faced by industries such as semiconductors, steels, tires, and shipbuilding with a specific focus on the application of AI. Manufacturing companies have recently shown a growing interest in using AI due to its promising results reported in papers, and they are eager to apply it in scheduling problems. In this talk, I will introduce real projects conducted with manufacturing companies, discuss the challenges of implementing AI or other heuristic algorithms in practical settings, and examine the advantages and limitations of using AI in comparison to optimization or meta-heuristic methods.
Keynote Speaker
Dr Hyun-Jung Kim
Associate Professor
Industrial & Systems Engineering,
Korea Advanced Institute of Science and Technology, Korea
Dr. Hyun-Jung Kim is an Associate Professor in the Department of Industrial & Systems Engineering at KAIST, Korea. Before joining KAIST in September 2019, she was an Assistant Professor in the Department of Systems Management Engineering at Sungkyunkwan University from 2015 to 2019. She earned her PhD from the Department of Industrial & Systems Engineering at KAIST in 2013. Her research focuses on the scheduling of manufacturing systems, modeling and analysis of discrete event systems, and smart and green manufacturing. Dr. Kim has received 'Best Semiconductor Manufacturing Automation Paper in Theory' from the IEEE Robotics and Automation Society's Technical Committee of Semiconductor Manufacturing Automation and an Honorable Mention for the IEEE Transactions on Semiconductor Manufacturing Best Paper Award. Additionally, she serves as an Associate Editor for the IEEE Transactions on Automation Science and Engineering and International Journal of Production Research.
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