Real-time scheduling for open-pit mining considering the impact of subsequent dispatching of trucks

In the production process of open-pit mines, haul trucks operate cyclically between loading and dumping points, undertaking the transportation of ore and waste materials. Therefore, developing efficient truck dispatching plans is crucial for improving mining production efficiency. Most existing dispatching methods only consider the impact of already-dispatched trucks while neglecting the queuing delays caused by dynamic demand on the destination selection of trucks awaiting dispatch, resulting in decreased production efficiency of open-pit mines. To address this issue, this paper proposes a real-time dispatching model that accounts for dynamic demand. The model leverages real-time data generated during the open-pit mining production process and optimizes real-time dispatching decisions for trucks awaiting dispatch by incorporating the expected waiting time arising from dynamic demand. A genetic algorithm is employed to simultaneously determine the optimal dispatching destinations for multiple trucks. Simulation tests based on a real open-pit mining environment demonstrate that, compared with three other models, the proposed model achieves at least a 9% increase in production output and at least a 9.6% reduction in transportation costs. Furthermore, the model exhibits strong adaptability when truck breakdowns occur during operation, further validating its effectiveness in complex production environments.