Pilot dispatching problem along a maritime corridor: a case study in the St. Lawrence River
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study presents a novel decision support process for a pilot dispatching problem in the St. Lawrence River. It integrates a comprehensive set of time-based performance measures, including working time, waiting time, and skill level differences, to optimize fairness and operational efficiency in pilot dispatching. The proposed process employs a weighted multi-objective model and a goal programming solution method to dynamically rank pilots, continuously updating dispatch plans. A year-long case study in the St. Lawrence River, Canada with 1288 vessels and 200 pilots across four stations showed that the proposed decision support process significantly improved workload distribution, reducing waiting times by 14% and enhancing pilot satisfaction. The findings highlight the potential for more balanced and efficient pilot dispatching approach benefiting for both service quality provided to vessels and the pilots themselves by reducing fatigue and improving performance measures.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it