Kuorma-autojen ja kauhakuormaajien toimintojen yhteensovittaminen avokaivoksissa käyttäen tilastollista dataa - ajojärjestysstrategiat, yhteensovituskerroin ja kaluston ikäperusteiset huollot
Bibliographic record
Abstract
Economics today force mining companies to maximize profit over the life time of a mine. Especially in the context of open-pit mines, it is essential to acquire production with minimum cost. The ability to reduce operation costs can be directly achieved by utilizing trucks and shovels in an efficient manner. Dispatching approaches in the literature have considered different objectives in varying degrees of sophisticated assignments ranging from simple heuristic rules to complex mathematical programming. However, most approaches assume that all trucks and shovels have the same operating performance or ignore the stochastic nature of the truck-and-shovel operations. This thesis investigates one of the primary problems in an open-pit mine: efficient matching trucks and shovels. In other words, the aim is to determine the required number of trucks and shovels and their types to make the best match in order to satisfy the production target. This problem is investigated using different simulation and optimization models, which contain the behaviours of dispatching strategy, match factor, and age-based maintenance under an ideal operation and breakdown event. The results of this thesis show that the match factor ratio is able to determine limits for an appropriate fleet size selection, and can be used to estimate the relative efficiency of existing fleets. However, it cannot be used alone for fleet optimization. The choice of truck dispatching strategies and heuristic truck dispatching methods plays a crucial role to minimize the queuing time. Maintenance schedules are necessary to reduce breakdown, directly influencing equipment availability. Optimal preventive and corrective maintenance schedules are proposed for different truck age levels, providing cost savings. These proposed models offer potential applications to any situations in which truck fleets are used to transport material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".