Scheduling Seminar: Scheduling with Machine Learning

Datum / čas
Date(s) - 01.03.
15:00 - 17:00

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Hyun-Jung Kim (Korea Advanced Institute of Science & Technology – KAIST)

Scheduling with Machine Learning


March 1, 2023 at 15 CET

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The abstract:
Manufacturing companies have recently shown a growing interest in using machine learning to improve scheduling problems. In this talk, we will present three real-life industrial scheduling problems faced by industries with a specific focus on the application of machine learning. First, in semiconductor manufacturing, multiple weighted dispatching rules are used to determine a sequence of jobs. Engineers assign these weights based on their previous experience. We propose a machine learning approach to determine the best weight set for all rules, especially when there is not enough time to derive it. Second, we propose an integration method of machine learning and mathematical formulation for scheduling problems in steel manufacturing. This approach reflects the engineers’ preferences and improves the performance of scheduling at the same time. Finally, we will present a hybrid flow shop scheduling problem for insulation manufacturing where machine learning with the NEH algorithm has been applied. We will also discuss the challenges of implementing machine learning or other heuristic algorithms in practical settings.


Hyun-Jung Kim is an Associate Professor with the Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST). She received B.S., M.S., and Ph.D. in industrial and systems engineering from KAIST. Her research interests include discrete event systems modeling, scheduling, and control.


The seminar is organized by Zdeněk Hanzálek (CIIRC CTU in Prague), Michael Pinedo (New York University) and Guohua Wan (Shanghai Jiao Tong).

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