2021/02/10 by William J. Reid, William Reid, Reid, William +6
Computer Science · Engineering · #Belt Conveyor Systems Engineering #FOS: Computer and information sciences #Machine Learning (cs.LG) #Mineral Processing and Grinding #Mining Techniques and Economics #Neural and Evolutionary Computing (cs.NE) #cs.LG #cs.NE
paper · pdf · doi:10.48550/arxiv.2102.05235
arxiv created 2021/02/10 · openalex publication_date 2021/02/10 · arxiv updated 2021/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we investigate the impact of uncertainty in advanced ore mine optimisation. We consider Maptek's software system Evolution which optimizes extraction sequences based on evolutionary computation techniques and quantify the uncertainty of the obtained solutions with respect to the ore deposit based on predictions obtained by ensembles of neural networks. Furthermore, we investigate the impact of staging on the obtained optimized solutions and discuss a wide range of components for this large scale stochastic optimisation problem which allow to mitigate the uncertainty in the ore deposit while maintaining high profitability.