Application of agent-based modeling to truck-shovel dispatching systems in open pit mines
Bibliographic record
Abstract
Various computer-based dispatching systems have been developed for managing truck and shovel pairing in surface mining operations and are being used to varying degrees of success. Systems have shown the ability to increase production and maintain ore quality within prescribed upper and lower limits provided there is a stable operational environment. However, operational environments in mining are uncertain and highly variable. Upsets such as equipment breakdowns, or changing weather conditions often occur and no claims have been made about the success of these systems to react to these upsets and successfully adapt to new operational conditions generated by upsets. In an ant colony different activities are performed simultaneously by specialized individuals. However when the environment with an ant colony changes or experiences a major upset, the configuration of task allocations within the colony change to adapt to the new conditions. In this thesis, the task allocation model developed for ant colonies was modified and used to develop a dispatch algorithm, the Agent Based Model, which reacts reliably to changes and upsets in surface mining operations. The algorithm was simulated over a twelve-hour shift using AUTOMOD®, a discrete event simulation program. The simulation results of the Agent Based Model are compared to that of the Fixed Assignment Method used by current dispatch systems in which each truck is permanently assigned to a particular shovel. The total production of ore and waste from the Agent Based Model is consistently greater than that from the Fixed Assignment Method because an Ant Based system allows re-assignment (task reallocation). The simulations also show that the Agent Based system reliably adapts and limits the impact of upsets on a mining operation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".