Extraboard transit operator scheduling considering driver absenteeism
Bibliographic record
Abstract
Public transit operators are known to display high absence rates due to the adverse health effects associated with driving transit vehicles. Transit agencies utilise extraboard operators to cover open work left by absent regular-duty operators. This study investigates the operational extraboard scheduling problem. We propose a two-stage stochastic program to address the uncertainty in unexpected open work when making scheduling decisions. In the proposed optimisation framework, the first-stage decisions are extraboard operators' report times for the next workday. After regular-duty operator absences are revealed, second-stage decisions assign the resulting open work to the extraboard operators such that uncovered open work is minimised. We employ sample average approximation to construct a finite-scenario approximation and solve the resulting model using an efficient Benders decomposition algorithm. Applied to a real-world case study, the proposed model is shown to outperform current methods in the literature. The case study also explores management practices, including extraboard roster sizing and the use of split-shift extraboard operators.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".