Stochastic mine production scheduling with multiple processes: Application at Escondida Norte, Chile
Bibliographic record
Abstract
Mining complexes can contain multiple mines operating simultaneously along with multiple processing streams, stockpiles and products. Stochastic optimization methods developed to date generate only local optimal solutions in the sense that they do not consider the entire mining complex. This paper presents an extension of a multi-stage method used for generating long-term risk-based mine production schedules, to operations with multiple rock types and processing streams. The developed method uses a simulated annealing based algorithm during the optimization stage, seeking to minimize deviations from production targets for waste and different ore processing streams. The proposed approach is applied at Escondida Norte copper deposit, Chile, in which sulphide, oxide, mixed and waste materials are present with milling, bio-leaching and acid-leaching being the available processing streams. The stochastic schedule generates expected deviations from mill and waste production targets smaller than 5%, which avoid indirect costs associated to idle capacities. A schedule generated conventionally exhibits expected deviations of the order of 20%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".