Bus Driver Rostering via Extending Group Multirole Assignment
Bibliographic record
Abstract
Although public transportation brings more and more convenience and practicality, it also presents greater safety hazards and economic concerns. How to select reasonable bus driver rostering (RBDR) for bus companies has become a pivotal resource optimization issue in public transportation by extending the Group Multirole Assignment (GMRA) model, this paper formalizes such a problem. Moreover, we propose multi-criteria decision making as a new method for driver evaluation, incorporating agent capability and satisfaction as important criteria. Additionally, in order to find the result solution more reasonably, the parameter change rules are obtained through multiple simulations. We first use the slope to find the steep drop point, and then use the variance and range to find the balance point, thereby obtaining the target parameter combination. The staged parameter selection method improves operating efficiency. Large-scale simulations indicate that the improved GMRA algorithm is suitable for different scenarios and can return multiple parameter combinations. By using this method, bus companies are able to select optimal parameter combinations in order to make diversified decisions based on transportation resources and development strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".