Optimisation of open-pit mine production scheduling considering optimum transportation system between truck haulage and semi-mobile in-pit crushing and conveying
Bibliographic record
Abstract
Despite the upward trend in using semi-mobile in-pit crushing and conveying system (SMIPCC), some mining companies still hesitate to use SMIPCC in their operations. Therefore, a framework is needed to facilitate decision-making between SMIPCC and truck and shovel system (TS) and satisfy the location and relocation time of the semi-mobile crusher (SMC). For this purpose, this paper presents a mathematical integer programming (IP) model considering the operational constraints. The model specifies both the optimum location and optimum relocation time of SMC. Also, it compares the production schedule for the TS and SMIPCC utilised for ore and waste handling over the life of mine. Then, the system with maximum net present value (NPV) is selected as the optimal material handling system. Finally, the model is implemented with a case study. The results show that implementing SMIPCC instead of the TS system for ore and waste materials improves the NPV by 69.77%.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".