Integration of inter-terminal transport and hinterland rail transport
Bibliographic record
Abstract
This paper investigates the problem of inter-terminal movements of containers and vehicles within a port area in order to achieve an integrated and effective transport within the port and towards the hinterland. Containers from different port terminals are first moved to a rail yard and then delivered to the hinterland by rail. To provide insights for stakeholders such as port authority and terminal operators into tactical planning problems, e.g., the coordination between terminals, railway timetable and train sizes, this paper proposes an optimization model describing the movement of containers and various vehicles between and inside terminals. The model aims at improving the container delivery from container terminals to the hinterland considering both railway hinterland transport and terminal handling operations. A network inspired by a real-life port area and its hinterland is used as a test case to test different components, i.e., inter-terminal transport connections, train formation, railway timetable. A rolling horizon framework is used to improve the computation efficiency in large transport demand cases. The result of the optimization helps in identifying the most promising features, namely, that more connections between terminals and a flexible outbound railway timetable could contribute to improving the integrated container transport performance.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".