**5. Conclusions**

This article has proposed a high-precision multi-vehicle collaborative scheduling proposition model considering micro-space-time factors to solve the unmanned dump truck scheduling problem in open-pit mines.

The optimization objective comprehensively considers energy consumption, time, and output. In addition to the loading and unloading activities, the unmanned dump truck also considers charging demand in the scheduling model. The model incorporates Voronoi graph search and optimal time matching of spatial paths. It aims to provide a better task decision planning solution for unmanned dump trucks in mining areas. To effectively solve the scheduling problem, an improved artificial bee colony algorithm is proposed. The original algorithm is enhanced in the global search process and the re-initialization process. An ablation experiment is conducted to explore the impact of these improvements on the optimization process. The ablation experiment result shows that changing these two items has promoted the optimization search. In addition, comparative simulation experiments are conducted using different algorithms, cost function definition strategies, and encoding strategies. Comparative simulations indicate the proposal can reduce energy consumption and time. Compared to the models utilizing ABC algorithms, cost function strategy definition without considering time, and binary encoding strategy, the proposed model and method achieved reductions of 0.67%, 3.1%, and 2.83% in the comprehensive cost functions of energy consumption, time, and output, respectively. Moreover, simulation

results demonstrate that the proposed model and method offer an effective solution for scheduling decisions in mining areas.

There are numerous factors that impact the overall cost of the unmanned dump truck scheduling problem. In the future, it is worth exploring the optimization of speed and scheduling arrangements in the event of unmanned dump truck failures. These areas present promising avenues for further research.

**Author Contributions:** Conceptualization, Y.F. and X.P.; methodology, X.P.; validation, Y.F. and X.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Natural Science Foundation of China under Grant 52275105.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The simulation data used to support the findings of this study are available from the corresponding author upon request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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