The Autonomous Haulage System (AHS) is an automated system in which trucks are controlled through computerized processes in which these trucks transport bulk loads to dump points in open-pit mining facilities. The main performance metric in such systems is the variability of cycle times, which refers to the variability in statistical terms of the amount of time taken by each truck to load, haul, unload, and return. Minimizing the variability of AHS routing is crucial for improving efficiency, safety, and utilization of the equipment in all mining value chains (Kolapo et al., 2025).
The main problem created by the variation of cycle times in open pit mines lies in the inefficiencies created as a result of the variability in the cycle times. In instances where these autonomous trucks take varying amounts of time in transport, clustering occurs at loading and dumping sites, leading to queuing of trucks that reduces the efficiency of the entire system due to idle equipment either from excavation or from waiting for loads (Sizemov et al., 2021).
The optimization of routing through dynamic algorithms allows avoiding operational problems caused by traffic bottlenecks. Unlike the previous method of route-optimizing used for vehicles, this dynamic algorithm utilizes several real-time factors such as traffic, health condition of the truck, and location of the rest of the fleet’s trucks at this very moment. Thus, through applying machine learning techniques and more sophisticated approaches in terms of route optimization, the truck may be rerouted to another loading area in case of congestion at the actual loading area.
Additionally, intelligent Fleet Management Systems play a key role in controlling the kinetics of vehicles with regard to routing optimization. Optimization of the route for Autonomous Hauling System is not limited to simply choosing the most convenient geographic route, but also includes the management of speed patterns of vehicles involved in the route. If the FMS identifies the fact that one truck is approaching the primary crusher when it is still busy, it instructs the autonomous truck to slow down before arriving.
The optimization of routing should take into account physical parameters existing in the particular environment. Autonomous trucks move under the restriction of a kinematic type concerning minimum turning radii, maximum gradient and truck’s braking capacity. It means that the optimization algorithms have to use information about geometries of haul roads not to assign routes leading trucks into unnecessary braking and acceleration cycles. The approach which considers these environmental features together with dynamics of the dispatching process increases predictability of mining processes (Sizemov et al., 2021).
To summarize, reduction of cycle time variation in surface mining calls for a multilateral approach to optimization of AHS. Integration of dynamic algorithms for path finding, automatic speed control due to centralized management of mining fleets, and consideration of geometrical constraints of haul roads help to minimize possible delays of mining processes. The improvement of digital dispatching systems will make the automation of a mine much more effective and will provide the mine with predictable operation (Kolapo et al., 2025).
References
Kolapo, P., Ogunsola, N. O., Komolafe, K., & Omole, D. D. (2025). Envisioning Human–Machine Relationship Towards Mining of the Future: An Overview. Mining, 5(5), 5. https://doi.org/10.3390/mining5010005
Sizemov, D. N., Temkin, I. O., Deryabin, S. A., & Vladimirov, D. Ya. (2021). On some aspects of increasing the target productivity of unmanned mine dump trucks. Eurasian Mining, 68-73. https://doi.org/10.17580/em.2021.02.15
