An optimization-based approach to fleet reliability and allocation in open-pit mining

Open-pit mining operations depend heavily on the availability and reliability of complex equipment fleets, where the failure of one component can disrupt overall productivity. This study proposes two complementary optimization models to enhance fleet allocation and reliability in the mining industry. The first model — a Mixed-Integer Nonlinear Programming (MINLP) formulation — supports short-term planning by maximizing the minimum reliability of heterogeneous truck–shovel sub-systems under production and utilization constraints. The second model focuses on medium-term reliability enhancement, allocating targeted reliability improvements to critical components based on equipment degradation and operational history. Both models are validated using real operational data from an open pit mine, which includes failure and repair time datasets from 17 trucks and 2 hydraulic shovels. Reliability curves are estimated using the power law model under a Non-Homogeneous Poisson Process (NHPP) assumption. Results show that optimal allocation can achieve production targets of 4,489 tons per hour with a minimum sub-system reliability of 0.7. Furthermore, reliability improvements tailored to engine-hour-based cost functions can effectively restore operational performance over a one-week horizon. This research bridges the gap between fleet allocation and reliability allocation and introduces a novel framework for integrating reliability into equipment planning. The models provide actionable insights for mining operations to optimize equipment deployment, reduce failure risk, and support more resilient and cost-effective planning. • Introduce optimization models to improve fleet allocation and reliability in mining. • Maximize system reliability while balancing production and fleet constraints. • Identify key components requiring reliability improvements over time. • Validate models with real-world data from an open-pit mining operation. • Bridge fleet allocation with reliability planning for better decision-making.