Optimisation de la planification à court et moyen terme dans les mines souterraines
Bibliographic record
Abstract
This thesis is part of the current trend of digitization in underground mines by addressing the problem of mine planning.The overall objective of the thesis is to provide a tool for short and medium-term optimization of plannings, allowing optimal solutions to be found in a short time.Underground mine planning for these time horizons is a difficult problem for a number of reasons, including the number of resources required, the large number of work places, the long-term implications of short-term decisions and the level of accuracy required.More specifically, the research objectives are to develop a mathematical programming model for short-term, another for short-and medium-term and a last one using constraint programming for the short-and medium-term and then compare the different approaches.A review of the available literature shows that the majority of work in mine planning is about open-pit mines.Even though they have some similarities, open-pit mines and underground mines are still too different to simply apply the solutions from one to the other.This discrepancy in the difference between the commercial offer of optimization products for both types of mines is another proof of this.Within the underground literature, the majority of publications focus on long-term planning.Some models are available for short-and mediumterm time horizons, but are mine specific.From this literature, all the models are based on mathematical programming, with the exception of one real-time planning model, but it adresses a very different problem from the one presented here.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".