An application of mathematical programming to determine the best height of draw in block-cave sequence optimisation
Bibliographic record
Abstract
Planning block-caving operations poses complexities in different areas such as safety, environment, ground control and production scheduling. The objective of this paper is to develop a practical optimisation framework for production scheduling of block-caving operations. A mixed-integer linear programming (MILP) formulation is developed, implemented and verified in the TOMLAB/CPLEX environment. In this formulation, the slices within each draw column are aggregated into selective units using a hierarchical clustering algorithm and the mining reserve is computed as a result of the optimal production schedule for each advancement direction. This paper presents a model application of a production schedule for 102 drawpoints with 3457 slices over 14 periods. The results show in order to obtain the maximum net present value (NPV), only 88% of the reserve is extracted. Also, the solving time for the presented method is 78 times faster than method without slice aggregation.
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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 | 0.001 |
| 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".