Solving a large SIP model for production scheduling at a gold mine with multiple processing streams and uncertain geology
Bibliographic record
Abstract
In this paper, an efficient heuristic solution approach is applied and tested to the stochastic mine production scheduling of a relatively large gold deposit containing about 120 thousand blocks and considering a set of 15 geological scenarios generated stochastically. The case study addresses multiple processing streams and a ‘grade’ stockpile, which adds flexibility to the specific operation by advancing the processing of highly valuable material. The solution approach first generates an initial feasible solution by sequentially solving the stochastic open-pit mine production scheduling (OPMPS) period by period, and then a network-flow algorithm is used to sequentially search for improvements. In this network graph, the nodes identify candidate blocks that might have their extraction postponed or advanced, aiming for new schedules with higher value and lower risks. The results show that production schedules with low deviations from production expectations can be generated in a reasonable time for an actual mining environment.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".