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 minimization (e.g., the travel times of the trains). The deadlock avoidance and detection problem is revisited, and a new deadlock avoidance algorithm, called DEADAALG, is proposed based on a resource reservation mechanism. The DEADAALG algorithm is proved to be exact; that is, it either detects an unavoidable deadlock resulting from the input data or provides train scheduling free of deadlocks with the scheduling algorithm SIMTRAS. Moreover, it is shown that SIMTRAS is a polynomial time algorithm with an O(|S| &Middot; |T| 2 log |T|) time complexity, where T is the set of trains and S is the set of sections in the railway topo logy. Numerical experiments are conducted on Canada's Vancouver–Calgary single-track corridor of Canadian Pacific Railway Limited. Then it is shown that SIMTRAS is efficient and provides schedules of a quality that is comparable with that 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 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".