Robust scheduling policy learning for ore-flow blending in dynamic and uncertain environments
Journal
Computers and Operations Research
ISSN
0305-0548
Date Issued
October 2026
Author(s)
Tang, Hao
Southeast University, The First People’s Hospital of Yunnan Province, University of Leicester, University of Manchester
Chen, Guoyu
Han, Shoufei
Li, Miqing
The University of Birmingham
Yuan, Liang
China Three Gorges University
Abstract
Ore-flow blending is essentially a real-time scheduling decision problem where equipment failures, sudden weather changes, and time-varying road network exist during production. This problem faces core challenges including high-dimensional spatiotemporal dynamics, real-time decision response, and long-horizon decision dependencies. To overcome these challenges, this paper designs an end-to-end ore-flow blending and truck scheduling framework based on a spatio-temporal graph policy network. By encoding the spatial topology of the road network and the dynamic temporal sequences of the ore flow into a unified state representation, the proposed model achieves scalable learning across road networks of varying scales and structures. Furthermore, a two-stage training strategy combining local short-horizon pre-training and global long-horizon fine-tuning is proposed, effectively resolving the computational and memory consumption challenges in large-scale, long-horizon scenarios. Experimental results on multiple real-world mine cases and dynamic scenarios demonstrate that this method significantly outperforms state-of-the-art techniques in ore flow stability control and exhibits strong robustness and generalization capabilities under different road network structures, equipment scales, and dynamic events.
Affiliation
Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, China
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 62476006, in part by the Hubei Provincial Natural Science Foundation of China under Grant 2023AFA049, and in part by the Fundamental Research Funds of the AUST under Grant 2024JBZD0007.
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