Simulation-based mine extraction sequencing with chance constrained risk tolerance
Bibliographic record
Abstract
Technical and financial uncertainties present significant risks to the profitability and efficiency of mining operations. Unexpected realizations (e.g., price or grade) may result in catastrophic consequences. This phenomenon forces mining industries to use probabilistic decision-making tools to assess, mitigate, and manage the risks associated with these uncertainties. In this context, mining operations need robust schedules, which are insensitive to market changes and/or unexpected grade realizations. The mine production scheduling problem consists of three sub-problems: extraction sequencing (timing), ore-waste discrimination (classification) and production rates (utilization). The solutions to these problems are generated under significant parameter uncertainties. This paper proposes an extraction sequencing approach in which the net present value of a mining project is, for a given risk tolerance, maximized and the actual risk tolerance is then verified through Monte-Carlo simulations. The risk tolerance is a measure of uncertainty and that secures the project net present value with a given probability. Risk tolerance is derived through the use of standard deviations of block economic values in the medium of multiple grade and economic images of orebody. The proposed approach is demonstrated on a case study using gold mine data. The results of the case study show that the proposed approach, combining chance-constrained programming and Monte-Carlo simulation, can be used to solve the mine extraction sequencing problem in an uncertain financial and technical environment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".