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<p>Ore dispatch in open-pit mining is a multidimensional stochastic optimization problem that requires dynamic decision-making to balance competing objectives: maximizing throughput, maintaining grade consistency, and reducing equipment queuing. This study formulates and evaluates a comparative reinforcement learning (RL) framework that learns adaptive dispatch policies in a simulated Internet of Things (IoT)-enabled open-pit mine. Three deep RL algorithms are implemented and compared: value-based dueling double DQN, on-policy proximal policy optimization (PPO), and maximum-entropy soft actor-critic (SAC). The results show that, although all algorithms can learn viable policies, SAC exhibits higher sample efficiency and more stable convergence, reaching strong performance in fewer simulated episodes. A detailed policy analysis reveals that the SAC agent learns an advanced non-intuitive strategy that improves grade control and yields balanced performance across objectives. Still, it does not consistently outperform a simple shortest-queue heuristic on all key performance indicators (KPIs), particularly throughput and queue minimization. This discrepancy exposes an agent–objective alignment problem and indicates that the hand-crafted reward function does not fully capture the underlying business priorities. The proposed framework, therefore, serves not only as an optimization tool but also as a diagnostic mechanism for exploring multi-objective trade-offs and revealing misalignment between engineered rewards and high-level KPIs, providing a practical basis for designing and validating autonomous dispatch systems prior to real-world deployment.</p>

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Navigating operational trade-offs in open-pit mining: a comparative reinforcement learning framework for adaptive ore dispatch

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月2日

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  • Azamat Umirzokov,
  • Aidar Kuttybayev,
  • Shukhratulla Ochilov,
  • Nurzod Nosirov,
  • Tursoat Amirov,
  • Bakhodir Kholiev,
  • Arystan Kozhantov,
  • Ma’ruf Karimov,
  • Khurshida Norova,
  • Ulugbek Urinov,
  • Umar Do’schanov,
  • Uchkun Eshonkulov

摘要

Ore dispatch in open-pit mining is a multidimensional stochastic optimization problem that requires dynamic decision-making to balance competing objectives: maximizing throughput, maintaining grade consistency, and reducing equipment queuing. This study formulates and evaluates a comparative reinforcement learning (RL) framework that learns adaptive dispatch policies in a simulated Internet of Things (IoT)-enabled open-pit mine. Three deep RL algorithms are implemented and compared: value-based dueling double DQN, on-policy proximal policy optimization (PPO), and maximum-entropy soft actor-critic (SAC). The results show that, although all algorithms can learn viable policies, SAC exhibits higher sample efficiency and more stable convergence, reaching strong performance in fewer simulated episodes. A detailed policy analysis reveals that the SAC agent learns an advanced non-intuitive strategy that improves grade control and yields balanced performance across objectives. Still, it does not consistently outperform a simple shortest-queue heuristic on all key performance indicators (KPIs), particularly throughput and queue minimization. This discrepancy exposes an agent–objective alignment problem and indicates that the hand-crafted reward function does not fully capture the underlying business priorities. The proposed framework, therefore, serves not only as an optimization tool but also as a diagnostic mechanism for exploring multi-objective trade-offs and revealing misalignment between engineered rewards and high-level KPIs, providing a practical basis for designing and validating autonomous dispatch systems prior to real-world deployment.