Robust Train Scheduling Problem with Optimized Maintenance Planning on High-Speed Railway Corridors: The China Case
Why this work is in the frame
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Bibliographic record
Abstract
Simultaneously considering train scheduling problem and maintenance planning problem with uncertain travel time, we propose a two-stage integrated optimization model for the sunset-departure and sunrise-arrival trains (SDSA-trains). Specifically, in the first stage, we obtain an optimal solution of the SDSA-trains under each scenario, which leads to the minimum total travel time. In the second stage, a robust SDSA-train schedule is generated based on the optimal solutions of the first stage. The key is that we consider two operation modes to solve the conflict between the SDSA-trains and the maintenances. Some state variables are used to deal with train operation mode selection. Furthermore, some linearization techniques are used to formulate a mixed-integer linear programming (MILP) model. Finally, numerical experiments are implemented to prove the effectiveness of the proposed model and optimization method.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it