An optimization-based approach to fleet reliability and allocation in open-pit mining
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".