Selection and Optimization of Heavy Earth Moving Equipment for Improved Mine Productivity

Equipment selection remains one of the most critical decision problems in earthmoving construction projects, with equipment-related expenditure accounting for more than 30% of total project cost and equipment emissions contributing substantially to the sector's environmental footprint. Choosing the right mix of trucks and excavators is driven by a tangled set of competing factors — unit work capacity, fleet size, travel speed, fuel consumption, and project deadlines — that make purely experience-based or manufacturer-chart selection methods unreliable at scale. This review provides a comprehensive optimization model for earthmoving equipment selection, structured around three sequential stages: (1) data definition, which organizes fixed costs, variable costs, contract specifications, and equipment specifications; (2) mathematical model formulation, which defines the relevant decision variables and constraints governing equipment choice; and (3) application of a genetic algorithm (GA) to solve the resulting multi-objective, multi-variable problem. Unlike prior equipment-selection models, truck travel speed is embedded as a primary decision variable, allowing the model to capture more realistic time-cost trade-offs than models that treat speed as fixed. (Halpin et al. (2017)) The model was tested and validated using data from a real-world earthmoving project, evaluating how total project cost varies with the time allowed for completion while simultaneously tracking fuel consumption and carbon emissions. Results show that the optimization approach can achieve average savings of 14.35% in project time and 9.5% in project cost, alongside meaningful reductions in fuel use and CO₂ output. (Sherafat et al. (2020)) By combining rigorous mathematical formulation with GA-based multi-objective optimization, this study offers contractors and construction managers a practical decision-support tool to balance time, cost, and environmental performance when planning earthmoving fleets. (Hummer et al. (2017))