Fleet selection for earthmoving projects using optimization-based simulation
Bibliographic record
Abstract
This paper presents a newly developed optimization simulation model for fleet selection for earthmoving operations. Global positioning system (GPS) data is used to build and update in near real time the developed model. The model is designed to assist contractors in selecting equipment fleet configurations for earthmoving operations; taking into consideration: (1) uncertainties associated with a set of quantitative variables that represent loading, hauling, and dumping duration, as well as, project direct and indirect cost; (2) availability of resources to contractors; (3) project cost and (or) time constraints; (4) project indirect cost; and (5) scope of work. The model allows contractors to assess the risk associated with the cost of the reconfigured fleet formations. The model has been implemented using commercial simulation software along with graphical user interface (GUI) module which was developed to incorporate the collected GPS data with the optimization simulation system. A commercial web based system is used to track the truck equipped with GPS in near real time. The system was rented during the period of conducting this research work. The developed model was applied to a construction project located in the west end of Montreal to demonstrate its use in optimizing earthmoving operations during construction.
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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 | 0.001 | 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) | 0.004 | 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".