Integrating short-term stochastic production planning updating with mining fleet management in industrial mining complexes: an actor-critic reinforcement learning approach
Bibliographic record
Abstract
Abstract Short-term production planning in industrial mining complexes involves defining daily, weekly or monthly decisions that aim to achieve production targets established by long-term planning. Operational requirements must be considered when defining fleet allocation and production scheduling decisions. Thus, this paper presents an actor-critic reinforcement learning (RL) method to make mining equipment allocation and production scheduling decisions that maximize the profitability of a mining operation. Two RL agents are proposed. The first agent allocates shovels to mining fronts by considering some operational requirements. The second agent defines the processing destination and the number of trucks required for transportation. A simulator of mining complex operations is proposed to forecast the material flow from the mining fronts to the destinations. This simulator provides new states and rewards to the RL agents, so shovel allocation and production scheduling decisions can be improved. Additionally, as the mining complex operates, sensors collect ore quality data, which are used to update the uncertainty associated with the orebody models. The improvement in material supply characterization allows the RL agents to make more informed decisions. A case study applied at a copper mining complex highlights the method’s ability to make informed decisions while collecting new data. The results show a 47% improvement in cash flow by adapting the shovel and truck allocation and material destination compared to a base case with predefined fleet assignments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".