Truck fleet size selection in open-pit mines based on the match factor using a MINLP model

The present study aims to propose new strategies based on mixed-integer non-linear programming (MINLP) models for the equipment sizing (ES) problem to verify the overall efficiency of the fleet. The developed models estimate the optimal size of trucks concerning the match factor value with two different strategies. The first strategy deals with each loader type, and the second one is applied simultaneously with all types of loaders. The proposed approaches are compared to a simulation strategy to assess the models. Implementing models with a copper mine case study provides a more efficient haul fleet size than the decisions offered by the simulation method. Moreover, the presented strategies provide an effective way to improve equipment performance where the current mine strategy does not adapt well. A key contribution of this research is the development, implementation, and verification of new optimization and simulation methods to address the ES problem in open-pit mines.