A simulation study of the passenger check-in system at the Ottawa international airport
Bibliographic record
Abstract
This work presents a simulation study of the check-in system at the Ottawa International Airport. Various data were collected and used to define the inputs to a simulation model. These include the current agents' working schedules, the passengers' arrival pattern distributions, the passengers' service time distributions, the historical flight load factor, the distributions of the types of passengers, and the flight schedule. The scenarios evaluated include changing the queue structure and considering alternate agents' working schedules. The performance measurement retained are the average waiting time in queues, the maximum waiting time in queues, the average queue length, the maximum queue length, and the distribution of passengers waiting times in queues. A linear programming (LP) model was developed to provide alternate agent working schedules that minimizes the total agent person hours and meets the passenger loads that vary throughout the day. A heuristic was used to incorporate breaks and lunches. The critical factor that impacts the check-in service performance proved to be the agents' working schedule. Sensitivity analysis on changes of passenger loads and service rates were performed and the findings are discussed.
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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".