Scheduled service network design with synchronization and transshipment constraints for intermodal container transportation networks
Bibliographic record
Abstract
In this paper we address the problem of scheduled service network design for container\nfreight distribution along rivers, canals, and coastlines. We propose a new concise continuous-\ntime mixed-integer linear programming model that accurately evaluates the time of occurrence\nof transportation events and the number of containers transshipped between vehicles. Given the\ntransportation network, the \neet of available vehicles, the demand and the supply of containers,\nthe sailing time of vehicles, and the structure of costs, the objective of the model is to build a\nminimum cost service network design and container distribution plan that denes services, their\ndeparture and arrival times, as well as vehicle and container routing. The model is solved with a\ncommercial solver and is tested on data instances inspired from real-world problems encountered\nby EU carrier companies. The results of the computational study show that in scheduled service\nnetworks direct routes happen more often when either the \neet capacity is tight or the handling\ncosts and the lead time interval increase. The increase of the same parameters leads to the\ndecrease of the number of containers transshipped between vehicles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".