Global asset optimization aims to simultaneously optimize mine production schedules, destination policies and the various processing streams in order to maximize the economic value over the life of a mineral resource supply chain. Conventional mine optimization approaches are incapable of incorporating geological uncertainty and may lead to severe deviations from forecasted production targets. Stochastic optimization models that manage risk in mine design and production scheduling have been developed over the past several years, however these models are often oversimplified, thus limited to provide only a local optimum in terms of the mining complex as a whole.
This paper addresses the issue of global optimization of open pit mine production schedules for complex mining supply chains under geological uncertainty. The proposed simulation-optimization framework builds on previous mining supply chain optimization work by permitting extraction decisions to be made simultaneously with material destination policies and processing stream decisions in order to maximize the value of the supply chain. The resulting framework is capable of modelling and efficiently optimizing over the non-linear intricacies that are often present in large mining complexes. The proposed optimizer uses a hybrid of both simulated annealing and particle swarm optimization. The method is tested on a copper-gold deposit and experimental results demonstrate that the optimizer is capable of generating production schedules and destination policies that reduce the risk of meeting production targets, have 14% higher net present value and increase the size of the final pit by 22%.
Paru en août 2014 , 28 pages