Shovel allocation and scheduling for open-pit mining using deep reinforcement learning
Bibliographic record
Abstract
The open-pit production system is a highly dynamic and uncertain environment with complex interactions between haulage and loading equipment on a shared road network. One of the key decisions in open-pit short-term planning is the allocation sequence of shovels to mining faces to meet the production targets established by long- and medium-term strategic plans. Deep Reinforcement Learning(DRL) techniques are commonly used in dynamic production environments. In this approach, an agent is trained on a simulation of the production system to learn the optimal decisions based on the system’s current state. This paper proposes a DRL approach based on the Deep Q-Learning algorithm to obtain a robust shovel allocation plan for open-pit short-term planning. First, a discrete-event simulation of the mining production system incorporating trucks, shovels, crushers, waste dumps, and the road network is created. This simulation models the uncertainties of each component’s operating cycle based on historical activity records, and it is used to train the DRL agent. The goal is to learn a robust shovel allocation strategy for the next production quarter, 3 months, to meet the tonnes per hour (TPH) production target to be delivered to the crusher feeds by interacting with the production simulator. The framework is tested in an iron ore open-pit mine case study where the shovel allocation agent successfully learns a strategy that consistently delivers the desired production target.
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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 | 0.000 |
| 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".