Abstract: Effectively managing and scheduling open-pit mine trucks can significantly improve transportation efficiency and reduce mining operation costs. Existing research focuses on using Deep Reinforcement Learning (DRL) to construct learning models for solving path optimization problems. However,when training models with Transformer architecture parameters,a large number of redundant parameters are generated. To address this issue,this paper proposes a lightweight graph attention mechanism for optimizing open-pit mine truck scheduling. Specifically,the Adams method,a numerical solution for differential equations,is employed in the weight learning of the Transformer model. A residual training method based on Adams is proposed to improve the optimization accuracy of the network in the later stages and further compress the model size,efficiently solving the open-pit mine truck scheduling optimization problem. The research shows that this method can reduce the optimal gap while compressing the parameter size of the source model to half,reducing the training dependency on GPU devices. Performance verification of the algorithm is conducted using randomly generated open-pit mine truck datasets,demonstrating that the Adams- Transformer model helps improve the efficiency of open-pit mine truck scheduling.

Key words: open-pit mine,optimization of truck scheduling,Adams method,graph attention mechanism,deep reinforcement learning