Benders decomposition to accelerate determination of optimal railway intervention programmes
Bibliographic record
Abstract
An important task of railway asset managers is to develop intervention programmes. These interventions need to be developed considering network-level synergies and constraints, in addition to the condition of the assets and their optimal intervention strategies. Considering these concerns may lead to executing interventions earlier or later than specified in asset intervention strategies to reach optimality. Synergies include the fact that the simultaneous execution of more than one intervention disrupts train movements only once. Constraints include budget limits and not closing parallel lines simultaneously. Although many railway asset managers currently determine intervention programmes in a rather qualitative iterative fashion, there is an increasing interest in exploiting digitalisation to improve the process. This interest has led to a rise in research focused on the development of mixed-integer linear programs to determine optimal programmes more efficiently and effectively. These powerful models, however, still have issues with complicated intervention planning problems, making their use slower than desired. This paper investigates the potential use of Benders decomposition to accelerate the determination of optimal railway intervention programmes for 2.2 km of the Irish Rail network. It is found that the optimal intervention programme is up to 30% faster for the studied example.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".