How to Improve the Efficiency of Check-In Counter: A Counter-Sharing Method
Bibliographic record
Abstract
Improving the efficiency of check-in counters is a significant concern for airport operation management departments and airline companies. However, the existing studies mainly optimize the counter allocation based on the number of counters and airport demands, which needs more exploration on improving the utilization rate of check-in counters under the condition of unchanged resource allocation. In this paper, we propose a counter-sharing method to improve check-in efficiency by sharing the idle check-in counters in the adjacent check-in area. In the proposed counter-sharing method, we first define the passenger’s total walking distance and waiting time for queues as the metrics, then reassign the check-in areas and adjust the internal departure sequence of flights to maximize the counter-sharing rate. The numerical results indicate that the proposed counter-sharing method can improve the efficiency of check-in counters and reduce the passenger’s total walking distance and waiting time in the queue during the check-in process, which can enhance the airport’s operation efficiency and competitiveness.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".