Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China
Bibliographic record
Abstract
In this study, a mixed integer programming model is proposed to address timetable rescheduling problem under primary delays. The model considers timetable rescheduling strategies such as retiming, reordering, and adjusting stop pattern. A genetic algorithm-based particle swarm optimization algorithm is developed where position vector and genetic evolution operators are reconstructed based on departure and arrival time of each train at stations. Finally, a numerical experiment of Beijing-Shanghai high-speed railway corridor is implemented to test the proposed model and algorithm. The results show that the objective value of proposed method is decreased by 15.6%, 48.8%, and 25.7% compared with the first-come-first-service strategy, the first-schedule-first-service strategy, and the particle swarm optimization, respectively. The gap between the best solution obtained by the proposed method and the optimum solution computed by CPLEX solver is around 19.6%. All delay cases are addressed within acceptable time (within 1.5 min). Moreover, the case study gives insight into the correlation between delay propagation and headway. The primary delays occur in high-density period (scheduled headway closes to the minimum headway), which results in a great delay propagation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".