Sublevel stope layout planning through a greedy heuristic approach based on dynamic programming
Bibliographic record
Abstract
Sublevel stoping is a commonly used underground mining method in which the profit can be increased by optimizing the layout plan. The complexity of the sublevel stope layout problem is demonstrated by showing it is a special case of the independent set problem, which is NP-hard. A novel approach based on dynamic programming is proposed to solve the sublevel stope layout problem. This approach identifies the recurring subproblems and memoizes their results. Memoizing subproblems reduces the solution time by shifting the computational burden of recalculation to the computer memory. To solve larger problem instances, a greedy heuristic is introduced to further decrease the solution time and limit the memory usage. For smaller problem sizes, the heuristic can be lifted and the approach can be used as an exact method. A large case study is presented to demonstrate the performance of the approach. The results show that that the stope layout plan is generated fast and it captures the valuable regions of the orebody well. A second, smaller case study is presented to benchmark the introduced exact and heuristic approaches. The exact approach outperforms the heuristic approach only by 1% while the solution time is reduced by more than 99%.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".