Surface constrained stochastic life-of-mine production scheduling
Bibliographic record
Abstract
The maximization of mining project discounted cash flows by defining the best sequence of extraction of underground materials requires understanding the availability of uncertain metal quantities throughout the deposit. This thesis proposes two versions of a stochastic integer programming formulation based on surfaces to address the optimization of life-of-mine production scheduling, whereby the supply of metal is uncertain and described by a set of equally probable simulated orebody models. The first version of the proposed formulation maximizes discounted cash flows, controls risk of deviating from production targets and is implemented sequentially, facilitating production scheduling for relatively large mineral deposits. Applications show practical intricacies and computational efficiency. The second variant extends the first to a two-stage stochastic integer programming formulation that manages the risk of deviating from production targets. The sequential implementation is considered first for pit space discretization and it is followed by the life-of-mine production scheduling at a relatively large gold deposit. The case studies show the computational efficiency and suitability of the method for realistic size mineral deposits, with production targets controlled, risk postponed to later stages of production and improvements in expected NPV, when compared to deterministic industry practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".