Comparaison d'approches de modélisation de problèmes tests pour le pilotage du transport : application aux mines à ciel ouvert
Bibliographic record
Abstract
This article aims to analyse the modelling concept used by researchers, in the benchmark-problems, for performance evaluation of control strategies in transport systems. For traffic management, two modelling approaches are used : macroscopic and microscopic models. However, for the dynamic fleet management, the majority of the benchmark-problem instances are modelled macroscopically. We prove that the macroscopic modelling can lead to biasing the simulation results, and thus cause a wide performance gap between the simulated and the real world. This gap is nowadays criticized by the managers of transport companies whom think that the researchers advance utopian results. We show the gap of performance between these two types of modelling on typical benchmark-problem instances of mining transport system. These instances are used to validate dynamic trucks dispatching algorithms. We also describe the object-oriented model which we developed to implement a microscopic simulator for mining transport system control. This object modelling approach allows the reusability of the microscopic simulator for further benchmark-problems.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".