A stochastic integrated simulation and mixed integer linear programming optimisation framework for truck dispatching problem in surface mines
Bibliographic record
Abstract
Making near optimal and close to reality decisions on the destination of trucks is vital for maximising the utilisation of truck and shovel fleets and subsequently minimising the operating costs in surface mines. We developed an integrated simulation and optimisation framework for solving truck dispatching problems in surface mines. The developed framework uses simulation modelling to imitate mining operations and capture technical uncertainties. It also applies uncertainty-based mixed integer linear optimisation modelling to dispatch trucks while capturing practical uncertainties. The developed optimisation model simultaneously optimises truck fleet utilisation, shovel fleet utilisation, and plant feed rate. The model considers the stochastic nature of the dispatching parameters and includes travel time uncertainties in the decision-making procedure. A comparison between the application of the developed optimisation model with a currently in the market optimisation model using the developed integrated simulation and optimisation framework showed 11% improvement in the production of the 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 | 0.000 |
| 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".