To address the scheduling challenges associated with the increasing deployment of battery-swapping trucks in open-pit mines, this study proposes a multi-objective scheduling optimization model. This model accounts for the unique characteristics of battery-swapping trucks by incorporating constraints related to battery swapping alerts, the selection of battery-swapping stations, and the impact of ambient temperature on battery capacity. The primary objective is to minimize the total haulage cost and total waiting time. Both a genetic algorithm and an adaptive genetic algorithm are applied to solve the proposed multi-objective scheduling optimization model. The aim is to identify an optimal scheduling solution without violating any model constraints. Results demonstrate that both the basic genetic algorithm and the adaptive genetic algorithm effectively achieve truck transportation scheduling. However, the adaptive genetic algorithm surpasses the basic genetic algorithm, reducing the total transportation costs by 5.6% and total waiting time by 17.4%. It also reduces the number of battery swaps and transportation distance by 15.8% and 1.2%, respectively. The proposed multi-objective scheduling optimization model successfully minimizes the waiting time and transportation costs of battery-swapping trucks while ensuring the completion of production tasks. This approach provides valuable technical support for improving the production and transportation efficiency of open-pit mining operations.

The roughness of haul roads significantly impacts fuel consumption in open-pit coal mine trucks, yet there is currently a lack of quantitative road classification methods in this regard. This study proposes a fuel-efficient road classification methodology for open-pit coal mines. Using UAV-captured point cloud data of mine roads as the basis for roughness analysis and the International Roughness Index (IRI) as the evaluation metric, the research establishes linear relationships between IRI and fuel consumption for both loaded and unloaded trucks. The K-means clustering algorithm is employed to classify road quality into “good”, “moderate”, and “poor” categories, with the Haerwusu Open-pit Coal Mine serving as a case study. Results demonstrate that 150 m represents an appropriate IRI segmentation interval for Haerwusu, with IRI thresholds of 12 (15) and 20 (21) serving as critical segmentation points for loaded (unloaded) trucks. From analyzing two end-slope roads in the case study mine we found that upgrading “poor” roads to “moderate” quality could reduce fuel costs by 3% for loaded trucks and 2% for unloaded trucks. This study provides a quantitative road classification method for open-pit coal mines, offering a theoretical foundation for reducing transportation costs and promoting sustainable mining development.