Evaluation of rail terminals in container ports using simulation: A case study
Bibliographic record
Abstract
The purpose of this research is to define the bottlenecks in a port’s rail container transport process and simulate the proposed scenarios in a real port using the lean-railroading approach. After visiting the selected port and holding several meetings with the authorities, all functions relating to containers have been identified. Each section’s arrival and departure times were used in this analysis for all containers shipped by rail in 2017, including 19,000 records. The data was accomplished by the processing time of transferring the containers between the railway areas and the port berth. The value stream map (VSM) related to the processes was prepared using the lean approach, the current port situation and proposed scenarios were simulated using the AnyLogic software. The results showed that the two proposed solutions effectively reduced time and costs by up to 20%. The research port also could increase the rail share to 8% without spending on infrastructure. Nevertheless, the rail container terminal warehouse will be a significant bottleneck for the port by growing the load operations. Reducing the turnover of the wagon will also halve the required number of wagons in the network. Therefore, it is recommended that a framework be established to direct freight to rail transport through the Ports Organization by meeting the infrastructure and operational requirements of the port and through the Railroad Company by launching the scheduled freight train and providing the required fleets.
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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) | 0.001 | 0.000 |
| 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 | 0.001 | 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".