Deadlock Avoidance and Detection in Railway Simulation Systems
Bibliographic record
Abstract
Avoiding or preventing deadlocks in simulation tools for train scheduling remains a critical issue, especially when combined with the objective of minimizing, e.g., the travel times of the trains. In this paper, we revisit the deadlock avoidance and detection problem, and propose a new deadlock avoidance algorithm, called DEADAALG, based on a resource reservation mechanism. The DEADAALG algorithm is proved to be exact, i.e., either detects an unavoidable deadlock resulting from the input data or provide a train scheduling thanks to the scheduling algorithm, called SIMTRAS, which is free of deadlocks. Moreover, we show that the SIMTRAS algorithm is a polynomial time algorithm with an O(|S| × |T|2log |T|) time complexity, where T is the set of trains and S is the set of sections in the railway topology. Numerical experiments are conducted on the Vancouver-Calgary single-track corridor of Canadian Pacific. We then show that the SIMTRAS algorithm is very efficient and provides schedules of a quality that is comparable to those of an exact optimization algorithm, in tens of seconds for up to 30 trains/day over a planning period of 60 days.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".