التفاصيل البيبلوغرافية
| العنوان: |
Scheduling of steelmaking-continuous casting process by integrating deep neural networks with mixed integer programming. |
| المؤلفون: |
Shin, Woo-Jin1 (AUTHOR), Lee, Sang-Wook1 (AUTHOR), Lee, Jun-Ho2 (AUTHOR), Song, Min-Ho3 (AUTHOR), Kim, Hyun-Jung1 (AUTHOR) hyunjungkim@kaist.ac.kr |
| المصدر: |
International Journal of Production Research. Dec2024, p1-22. 22p. 14 Illustrations. |
| مصطلحات موضوعية: |
*ARTIFICIAL neural networks, *ARTIFICIAL intelligence, *INTEGER programming, *FLOW shops, TRAINING of engineers |
| مستخلص: |
This study addresses the scheduling problem in the steelmaking-continuous casting (SCC) process. The SCC process is a hybrid flow shop with three stages, and we focus on job dispatching in the second stage, the refining stage. Our primary aim is to develop an algorithm applicable to real-world scenarios, mirroring field engineers’ decision-making and handling the process’s complex features. We propose a deep neural network (DNN)-based approach, trained on engineers' past decisions, achieving up to 97% accuracy. However, DNN alone falls short of outperforming engineers in scheduling objectives, specifically minimizing the total completion time in the refining stage. Hence, we introduce a novel approach combining DNN with mixed integer programming (MIP). In the integrated approach, the DNN initially makes decisions, but when confidence in the accuracy of a DNN-based decision is lacking, as determined by a developed reliability measure, it is supplemented with a decision derived using MIP. Experiments demonstrate that this integration improves scheduling objectives, surpassing engineers' performance. Furthermore, filtering inaccurate decisions enhances the accuracy of the DNN-based decisions. The proposed approach has been successfully implemented in one of South Korea's largest steelmaking companies. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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