Simulation Optimization of Shovel‐Truck System in Open‐Pit Mines Considering Rockmass Parameters
Bibliographic record
Abstract
The shovel‐truck system remains a popular method for overburden removal and mineral excavation in open‐pit mines, needing rigorous logistical management to achieve required productivity levels and maximize resource utilization. Fixed truck assignment (FTA) models represent a prevalent method for truck allocation in open‐pit mining, owing to their simplified fleet operational management. However, existing FTA models often overlook the simultaneous minimization of both trucks’ waiting time and shovels’ idle time. Consequently, these oversights lead to suboptimal allocation of trucks to shovels, resulting in either trucks queuing or shovels idling while awaiting trucks. Such inefficiencies contribute to fleet underutilization and increased fuel costs. To tackle the above issue, this research introduces a novel truck dispatching rule, MFTA, which integrates geotechnical parameters and excavating equipment performance to optimize truck allocation in open‐pit mining. Geotechnical parameters across various rock and soil formations reveal significant variability, influencing shovel performance assessed through the total loading time (TLT) indicator. Utilizing TLT and travel times of loaded and empty trucks, the study determines the optimal number of fixed trucks allocated to each shovel by minimizing the total waiting time (TWT). A case study conducted in an open‐pit coal mine in Thar, Pakistan, validates the approach, demonstrating that adjusting truck allocations based on TLT significantly reduces operational inefficiencies and enhances productivity. The findings highlight the effectiveness of this method in improving overall operational efficiency and economics in open‐pit mining. Integrating real‐time data and advanced simulation techniques, this research enhances the competitiveness and sustainability of mining operations. These outcomes are particularly relevant for mining professionals aiming to optimize mining operations for improved efficiency and sustainability.
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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".