Application of Discrete-Event Simulation for Truck Fleet Estimation at an Open-Pit Copper Mine in Peru

Abstract

With production expansions, studies related to estimating the number of pieces of hauling equipment to be employed in open-pit mines have to be carried out. One of the main challenges comes from the methodology selected since numerous tools are available, including commercial solutions. However, given that some methodologies were complex or required an advanced understanding of programming languages and that, in the case study, the mining company was applying a deterministic approach, a stochastic methodology that involves a discrete-event simulation (DES) was proposed. Such an approach aimed to develop a calibration model whose inputs incorporated random variables, such as fixed times and tonnages loaded to hauling equipment. This model supported the replication of the yearly production plan for an open-pit copper mine in Peru located at 4500 masl that is expanding its operations in 2023 from 100,000 tons per day to 140,000. The results obtained from the stochastic methodology were compared with the deterministic approach, which showed that the stochastic model required additional trucks and that longer cycle times were generated from such an approach. Such outputs are now supporting engineers in anticipating future problems in the mine due to the generation of longer queues.