Abstract
As part of the design of Medium-Term plans, hauling routes are traditionally outlined manually, which makes this task significantly time consuming. In each period, the hauling network is updated to route the targeted mining areas to their respective destinations, with a focus on optimizing travel time. Furthermore, it is essential to project the end-of-period topographic surface, accounting for the updated hauling routes. To automate this process, an algorithm based on pattern recognition techniques and shortest path optimization is proposed.
The algorithm comprises three primary steps: 1. identifying in- and ex-pit areas, 2. optimizing the shortest path, and 3. updating the end-of-period surface. The algorithm starts off by identifying regions that are inside and outside the mining operations. In the next step, a customized version of the Dijkstra algorithm connects the targeted regions to the hauling routes outside the pit regions. Lastly, the end-of-period surface is adjusted according to the proposed hauling network. The algorithm is designed to assist mine planners in the evaluation of hauling routes. The time saved by automating this task permits mine planners to explore additional mining scenarios.