Dynamic Allocation of Airline Check-In Counters: A Queueing Optimization Approach
Bibliographic record
Abstract
This paper was motivated by an observation in an international airport with regard to allocation of resources for check-in counters. In an exclusive check-in counter system, each flight has a dedicated number of counters that will be open until at least a half-hour before the scheduled departure of that flight. Currently, in many of the airports around the world, the decision to open or close check-in counters is done on an ad hoc basis by human schedulers. In doing so, the schedulers are almost always forced to perform a balancing act in meeting the quality of service stipulated by the airport authority vis-à-vis the optimal allocation of the resources to the counters. There appear to be very few academic and application papers in counter management, and most of those that have looked into this problem have resorted to simulation to study the queue characteristics. Ours is the first paper to show that for a specific flight, this complicated problem is amenable to analytical treatment. We first propose a multicounter queueing model with a special type of arrival process reflecting reality from the population of passengers booked for the flight. Most importantly, we derive the time-dependent operating characteristics to the queueing process under a specified time-window constraint. Then a stochastic dynamic programming model is formulated to determine the optimal numbers of counters to open over the time window specified. A numerical example is provided to illustrate the model solution and gain managerial insights.
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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 | 0.001 | 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".