ABSTRACT Deployment of autonomous driving trucks (ADTs) in open‐pit mining enables more flexible transport operations than human‐driven trucks; but this flexibility significantly expands the scheduling search space and makes it more difficult to find near‐optimal solutions on large scales. To address this challenge, this study proposes a bi‐layer divide‐and‐conquer framework for large‐scale ADT scheduling. In the upper layer, a mixed‐integer linear programming model is formulated to determine the minimum fleet size (FS) required to satisfy production targets within a shift, thereby reducing the dimensionality of the scheduling problem. Given the optimised FS, the lower layer solves the scheduling problem using a reinforcement learning (RL)‐assisted evolutionary programming approach. Specifically, a deep Q‐network is embedded in a genetic algorithm to adaptively adjust the crossover and mutation probabilities, improving search efficiency and enhancing the algorithm's ability to escape local optima. Experiments based on real‐world mining scenarios in Inner Mongolia including over‐100‐truck scenarios show that the proposed framework can reduce FS by more than 10% and fuel consumption by over 20% compared to current methods. Overall, the framework reduces problem dimensionality without compromising optimality and supports efficient scheduling for large‐scale mining operations.
Sun et al. (Thu,) studied this question.