A New Ant Colony Optimization Algorithm to Solve the Periodic Capacitated Arc Routing Problem with Continuous Moves
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Bibliographic record
Abstract
This paper describes a variant of the Periodic Capacitated Arc Routing Problem for inspections in a railroad network. Inspections are performed by vehicles over a time horizon on which some stretches need evaluation more frequently than others due to its use. Each car can evaluate one stretch per day without being attached to a depot; at each day, the shift may start and end at different locations. This characterizes the problem as the Periodic Capacitated Arc Routing Problem with Continuous Moves in which firstly the delays on attendances are minimized and, second, the displacement costs. We present a mathematical model and an Ant Colony Optimization algorithm to solve the problem. The use of a local search procedure and some principles of Granular Tabu Search is crucial for the algorithm’s performance. The numerical results are promising, especially for critical situations where the arcs’ needs are close to the total vehicles’ capacity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it