Algorithmic approach to pushback design based on stochastic programming: method, application and comparisons
Bibliographic record
Abstract
Pushback design affects the way a mineral deposit is extracted. It defines where the operation begins, the contour of the ultimate pit, and how to reach such ultimate contour. Therefore, different pushback designs lead to differences in the net present value (NPV) of a project. It is important to find the optimal pushback design which maximises the NPV. Conventional approaches to designing pushbacks lead to not meeting production targets and NPV forecasts. This is mainly due to the lack of integrating uncertainty into the process. Recent efforts have shown that the integration of uncertainty into production scheduling results in NPV increases in the order of ∼25%. The purpose of this research is to make use of a stochastic integer programming model to integrate uncertainty into the process of pushback design. The approach is tested on porphyry copper deposit. Results show the sensitivity of the NPV to the design of starting and intermediate pushbacks, as well as the pushback design at the bottom of the pit. The new approach yielded an increment of ∼30% in the NPV when compared to the conventional approach. The differences reported are due to different scheduling patterns, the waste mining rate and an extension of the pit limits which yielded an extra ∼5500 t of metal.
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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 | 0.006 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".