Two new mixed‐integer programming models for the integrated train formation and shipment path optimization problem
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Railcars are known as the heart of the freight rail transportation industry. Hence, any improvements in their operations can lead to sharp reductions in various operating costs. One of the most critical operations on railcars is blocking and routing their transportation. Railway companies face continuous challenged about what blocks should be formed to carry shipments across different origin–destination pairs (O–D pairs) and reclassify them in intermediate yards to minimize transportation reclassification costs. In addition, it is necessary to determine train service between O–D pairs and the number of trains. Along with the shipment routing plans, this problem is called train formation and shipment path optimization (TFSP). In TFSP, some substructure and rail network operational constraints should be considered, including link capacity, classification capacity, the number of sorting tracks, and path length. This paper presents two arc‐based mixed‐integer linear programming (MILP) models to formulate the TFSP problem. To the best of the authors' knowledge, no MILP arc‐based model has been published for the problem that does not need any preprocess before solving. Computational results of solving models on the datasets showed that the first model could obtain a feasible solution with a maximum 0.05% gap up to 48 yards instance. The second model also could find a solution with a small gap compared to the optimal solution in a reasonable time for instances up to 128 yards. Also, the proposed models were compared to the best methods in the literature, and their superiority was shown.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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