Short-term planning optimisation of open pit mines with Monte-Carlo haulage simulation in presence of semi-mobile IPCC
Bibliographic record
Abstract
In-pit crushing and conveying (IPCC) is emerging as a viable alternative to traditional truck-shovel haulage in open-pit mines, driven by the rising fuel cost and concerns over greenhouse gas (GHG) emissions. Effective short-term planning in open-pit mines must account for operational and equipment uncertainties. However, optimising short-term planning with IPCC integration is a relatively underexplored research area. This study addresses this gap by developing a novel simulation-optimisation framework that combines mixed-integer linear programming (MILP) and Monte Carlo simulation (MCS) to simultaneously optimise short-term production schedules and evaluate haulage system performance under uncertainty. This framework explicitly incorporates IPCC operations and their failure-related uncertainties, an aspect largely overlooked in existing short-term planning models, into the planning process. The MILP minimises haulage costs while generating schedules and meeting long-term production targets through optimal shovel allocation to mining cuts. These schedules are then input into a Monte Carlo haulage simulation model, which captures the uncertainties related to trucks, shovels, and IPCC operations. Additionally, the simulation estimates key performance indicators including maximum tonnes per gross operating hour (TPGOH) and the proximity to optimal production under uncertain conditions for both IPCC and truck-shovel scenarios. The model has been verified through a case study in an iron ore mine over a 12-month planning horizon, yielding promising results that support the adoption of semi-mobile IPCC systems over traditional truck-shovel operations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".