Optimization of Container Terminal Layouts in the Seaport—Case of Port of Montreal
Bibliographic record
Abstract
The intermodal transportation system plays a pivotal role in a global supply chain. Despite the benefits of intermodal transportation, it still has negative impacts, which are associated with congestion and emission. Greenhouse Gas (GHG) emission is highly associated with the transportation industry, and the share of the transportation sector is growing at a rapid pace. This paper discusses intermodal transportation and its effects on port efficiency. We proposed a new layout for the container terminal in the seaport in order to decrease cost as well as emission generated by the port operations. The proposed layout can improve the sustainability of port activities by decreasing the distance between the berth and interface points as well as avoiding double handling. We propose multi-objective optimization using the ε-constraint method to solve this problem. The model was tested through a case study of the Port of Montreal in Canada. The findings reveal that the proposed layout resulted in considerable emissions and costs reduction. Compared with the current layout of Port of Montreal, the proposed layout achieved a reduction of 46.5% in the total transportation cost of the containers to their final destination, as well as 21.6% in the emission.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".