Analysing equipment allocation through queuing theory and Monte-Carlo simulations in surface mining operations
Bibliographic record
Abstract
Shovels and trucks are widely used in earth moving and surface mining operations as a materials handling system. Insufficient equipment allocation for a given fleet results in not achieving production targets, high production costs and opportunity costs associated with shovel idle times or truck queues. Match factor is commonly used to measure the compatibility among trucks and shovels in terms of fleet size, truck cycle and shovel loading times. The calculated match factor is a deterministic value and does not reflect the sensitivities to unexpected variations of cycle, loading and waiting times. In this paper, the effects of uncertainties associated with shovel loading, truck waiting times, truck cycle times and fleet availability on match factor are assessed. In doing so, queuing theory is applied to model the waiting times for trucks, and Monte-Carlo samplings are used to model fleet availability, shovel waiting and truck cycle times. The proposed approach is demonstrated through a case study.
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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 | 0.001 |
| 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".