A hybrid MARL clustering framework for real time open pit mine truck scheduling

This paper proposes an innovative approach that combines a QMIX algorithm (a multi-agent deep reinforcement learning algorithm, MADRL) with a Gaussian Mixture Model (GMM) algorithm for optimizing intelligent path planning and scheduling of mining trucks in open-pit mining environments. The focus of...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 34875 - 23
Main Authors: Xiang, Xiaolei, Lin, Wei, Li, Danqi
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 07.10.2025
Nature Publishing Group
Nature Portfolio
Subjects:
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639/166/988
Algorithms
Anniversaries
Automation
Collaboration
Decision making
Deep learning
Efficiency
Gaussian mixture model
Humanities and Social Sciences
Linear programming
Mining
Multi-agent deep reinforcement learning algorithm
multidisciplinary
Network management systems
Open-pit mining
QMIX
Queuing theory
Reinforcement
Scheduling
Science
Science (multidisciplinary)
Traffic congestion
Truck scheduling
Trucks
Truck scheduling
Multi-agent deep reinforcement learning algorithm
QMIX
Gaussian mixture model
Open-pit mining
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:This paper proposes an innovative approach that combines a QMIX algorithm (a multi-agent deep reinforcement learning algorithm, MADRL) with a Gaussian Mixture Model (GMM) algorithm for optimizing intelligent path planning and scheduling of mining trucks in open-pit mining environments. The focus of this method is twofold. Firstly, it achieves collaborative cooperation among multiple mining trucks using the QMIX algorithm. Secondly, it integrates the GMM algorithm with QMIX for modeling, predicting, classifying and analyzing existing vehicle outcomes, to enhance the navigation capabilities of mining trucks within the environment. Through simulation experiments, the effectiveness of this combined algorithm was validated in improving vehicle operational efficiency, reducing non-working waiting time, and enhancing transportation efficiency. Moreover, this research compares the results of the algorithm with single-agent deep reinforcement learning algorithms, demonstrating the advantages of multi-agent algorithms in environments characterized with multi-agent collaboration. The QMIX-GMM mixed framework outperformed traditional approaches as complexity increased in the mining environments. The study provides new technological insights for intelligent planning of mining trucks and offers significant reference value for the automation of multi-agent collaboration in other environments. The limitation has been with regard to the maximum fleet size considered in the study being suitable to small or mid-scale mines.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-16347-0