<p>In underground mining, the efficient scheduling of trackless vehicles for cargo logistics is crucial due to its significant impact on operational costs and productivity. Traditional methods often fail to address the dynamic and complex nature of underground environments, leading to inefficiencies. To tackle this issue, we introduce a Particle Swarm Optimization (PSO) algorithm to optimize the scheduling of these vehicles. The primary aim of our study is to enhance the overall efficiency and reliability of cargo logistics in underground mines. We developed a scheduling model that considers various constraints such as vehicle capacity, travel time, and loading/unloading operations. Through extensive simulations and real-world data from a mining site, we found that the PSO algorithm significantly outperforms traditional scheduling methods in terms of reducing total travel time and improving resource utilization. Our results demonstrate that the PSO-based scheduling approach can achieve near-optimal solutions within a reasonable timeframe, thereby offering a robust and scalable solution for underground mine logistics. These findings suggest that incorporating PSO into vehicle scheduling systems provides a viable path for improving operational efficiency and reducing costs in the mining industry.</p>