Sustainable open pit fleet management system: Integrating economic and environmental objectives into truck allocation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fleet management systems in open pit mines make two important semi-dynamic and dynamic decisions to maximize utilization of available equipment: the decision of allocation and the decision of dispatching the trucks to the shovels. In this paper, we propose a bi-objective mathematical model that incorporates the minimization of carbon emission into the allocation optimization model. We also consider different inputs that might impact upon truck allocation decisions such as the fleet size, truck velocity, truck age groups, etc. The presented mathematical model is examined using two different case studies from an iron mine and a copper mine containing a different number of shovels, dumps, and trucks. The results reveal that the developed model enhances the production performance while controlling emissions. It is indicated that the average truck velocity and, the age of trucks are among the critical factors, which can highly affect the amount of carbon emissions.
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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 it