Analysis of a methodology for simulating a port logistics system to evaluate rail capacity in bulk ports
Bibliographic record
Abstract
With the growth of the maritime industry, which is a key sector for many supply chains worldwide, ports are looking to increase their capacity and performance in order to meet the challenges and opportunities created by the increased demand. Among various possible avenues, port expansion is one approach to achieving the objective of increased capacity, but requires significant resources and careful planning. Before undertaking a project of this scale, it is important to have a good understanding of the port's current capacity as well as the requirements and limitations of an expansion project based on historical data and future forecasts. The objective of the research project presented in this paper is to develop a methodology for predicting bottlenecks and evaluating capacity in the context of a port expansion project. Moreover, the proposed methodology addresses the particular case of ports with limited visibility and data on their operations, which constitutes an additional challenge for port organizations. With the use of a quantitative approach based on discrete-event simulation, the proposed methodology is applied to the case study of a Canadian port.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".