Multi‐agent system for the reactive fleet maintenance support planning of a fleet of mobile cyber–physical systems
Bibliographic record
Abstract
Improving the availability and reliability of a fleet of mobile cyber–physical systems as well as the ability to adapt the maintenance planning decisions when facing unexpected events at the fleet level is a major stake faced by the manufacturers and the operators. In the context of this study, the authors propose a reactive multi‐agent system model for the fleet maintenance planning of mobile cyber–physical systems. For that purpose, the ANEMONA multi‐agent design methodology is used. In this methodology, the agents are modelled and then their organisational and interaction views are described. Numerical experiments are carried out in static and dynamic contexts. In a static context, the proposed multi‐agent system is compared with a mathematical programming model to validate the effectiveness of the former in satisfying the fleet's availability and reliability expectations. In a dynamic context, simulated perturbations are used to illustrate the reactivity of the proposed multi‐agent system. Lastly, an application to rail transport for the maintenance of a fleet of trains at Bombardier Transportation France is proposed. For that purpose, the proposed multi‐agent system is integrated in the model layer of a decision support system named ‘MainFleet’ which is currently under development.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".