Developing the seaport container terminal layout to enhance efficiency of the intermodal transportation system and port operations – case of the Port of Montreal
Bibliographic record
Abstract
The intermodal container terminal is considered the hearth of intermodal transportation systems, where different transportation modes meet to exchange containers. Using intermodal transportation efficiently reduces the handling procedures and relieves congestion at every transfer site. The efficient container terminal operations play a significant role in enhancing container flows at the container terminal. One way to enhance port operations efficiency and facilitate smooth container flow is to improve the intermodal container terminal layout. An efficient container terminal layout can be achieved through a reduction in the distance between the quay and rail tracks. We propose a novel layout to reduce the cost and accelerate container flow, thus improving the effectiveness of the container terminal. A mathematical model is developed to evaluate both layouts' performance considering congestion between nodes throughout the network. The Montreal Port is used as a case study to validate the proposed layout and the experiments conducted. It has been found that the proposed layout resulted in significant reductions in cost and time. Comparative results between the proposed layout and that of the Montreal Port demonstrates a reduction of 10% and 8% respectively to the total cost and time of transport containers from the port to their final destination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".