Scheduling algorithm with controllable train speeds and departure times to decrease the total train tardiness
Bibliographic record
Abstract
The problem of generating a train schedule for a single-track railway system is addressed in this paper. A three stage scheduling is proposed to reduce the total train tardiness. We derived an appropriate job-shop scheduling algorithm called DR-algorithm. In the first stage, by determining appropriate weights of the dispatching rules, a pre-schedule is constructed. In the second stage, on the basis of the pre-schedule, the departure times of the trains are modified to reduce the number of conflicts in using railway sections by different trains. In the third stage, a train speed control helps the scheduler to change the trains' speeds in order to reduce the train tardiness and to reach other objectives. The factual train schedule is based on the modified train speeds and on the modified departure times of the trains. The experimental running of the DR-algorithm on the benchmark instances showed this algorithm can solve train scheduling problems in a close to optimal way. In particular, the total train tardiness was reduced about 20% due to controlling train speeds and the departure times of the trains.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".