Scheduling of steelmaking-continuous casting process by integrating deep neural networks with mixed integer programming

Woo-Jin Shin, Sang-Wook Lee, Jun-Ho Lee, Min-Ho Song and Hyun-Jung Kim

International Journal of Production Research, 2025, vol. 63, issue 11, 4180-4201

Abstract: 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.

Date: 2025
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DOI: 10.1080/00207543.2024.2439369

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