State-of-the-art optimization methods for short-term mine planning
Bibliographic record
Abstract
Maintaining short-term planning aligned with the ultimate long-term plan is challenging. This requires many details to be modelled on a daily or weekly basis to reach this target. Short-term planning is more challenging than medium- and long-term planning because it deals with daily challenges with block model changes, mining cut polygons variation, which increases the gap between medium- and long-term plans for each material to be mined. Short-term mine planning teams are expected to identify and manage potential risks to mitigate them, and eventually achieve the long-term objective of maximizing the Net Present Value (NPV). Very few studies have identified the problems that exist in short-term mine planning and provided technical solutions to overcome them for open-pit mines. One of the complexities associated with short-term planning is the creation of polygons or mining cuts by clustering before optimizing and scheduling the plan to reduce the computational expense of mine planning models. The primary objective of this study is to review the latest papers describing short-term mine planning challenges and technical solutions proposed to optimize mine planning for open-pit mines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".