A decision-support framework for fleet selection under loader removal and truck reallocation scenarios

Effective fleet management in open-pit mining necessitates flexible decision making to balance production targets, equipment utilization, and operational expenses. This research presents a decision-support model that combines mathematical optimization with discrete-event simulation to enhance fleet selection and allocation under dynamic operational scenarios. In contrast to conventional models that presume static fleet configurations, the suggested framework directly incorporates loader removal and truck reallocation as tactical measures to improve overall hauling efficiency. The optimization component identifies feasible fleet configurations through loader removal and truck reallocation, the match factor concept, and other constraints, whereas the simulation element assesses their performance under stochastic variations in the mining operations. The framework was applied to the Sungun Copper Mine in Iran as a practical case study. Findings show that removing two underperforming loaders and reallocating their trucks to the other loading units boosted total production by 41%, enhanced the overall match factor from 49% to 60.34%, and resulted in better fleet use without requiring extra capital investment. The suggested framework offers a practical, data-driven tool for mining engineers to adapt fleets dynamically and make informed operational choices under uncertain conditions.