Automating and optimising pushback selection using reinforcement learning
Bibliographic record
Abstract
Pushback optimisation, an early phase of strategic open pit planning, shapes mining phases that guide extraction for the mine's life and strongly influences operational feasibility and economic value. The task unfolds in two steps: first, a set of nested pits is generated; second, a subset of these pits is chosen, defining the pushbacks. Selecting pushbacks is hard because it must meet several operational criteria and has an impact on the production schedule, and thus the project's NPV. Existing tools offer partial help but still depend on manual judgment and rough schedules, often preventing optimal solutions. This study presents an automated pushback-selection method built on reinforcement learning (RL). An RL agent learns, through interactions with the nested-pit environment, to select the pushback set that maximises NPV while respecting key constraints, minimum mining width (MMW), waste-to-ore ratio, balanced tonnage swings, and full use of mining and processing capacities. The framework is tested on the publicly available McLaughlin mine dataset, with emphasis on maintaining the MMW constraint. Results show the RL approach is efficient and economically superior: it produces pushbacks whose NPV is 9% higher than mining every nested pit sequentially. These outcomes underscore RL's promise for automated, constraint-aware pushback optimisation.
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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 | 0.001 | 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".