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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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 34875 - 23
Hauptverfasser:	Xiang, Xiaolei, Lin, Wei, Li, Danqi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 07.10.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166 639/166/988 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-Zugang:	Volltext
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