Dispatch with Confidence: Integration of machine learning, Optimization and Simulation for Open Pit Mines
Kosta Ristovski (Hitachi America Ltd.);Chetan Gupta (Hitachi America Ltd.);Kunihiko Harada (Hitachi America Ltd.);Hsiu-Khuern Tang (Hitachi America Ltd.)
Open pit mining operations require utilization of extremely expensive equipment such as large trucks, shovels and loaders. To remain competitive, mining companies are under pressure to increase equipment utilization and reduce operational costs. The key to this in mining operations is to have sophisticated truck assignment strategies which will ensure that equipment is utilized efficiently with minimum operating cost. To address this problem, we have implemented truck assignment approach which integrates machine learning, linear/integer programming and simulation. Our truck assignment approach takes into consideration the number of trucks and their sizes, shovels and dump locations as well as stochastic activity times during the operations. Machine learning is used to predict probability distributions of equipment activity duration. We have validated of the approach using data collected from two open pit mines. Our experimental results shows that our approach offers increase of 10% in efficiency. Presented results demonstrate that machine learning can bring significant value to mining industry.