Event‐based allocation of airline check‐in counters: a simple dynamic optimization method supported by empirical data
Bibliographic record
Abstract
Abstract This paper studies the real‐life problem of dynamically optimizing the number of airport check‐in counters to allocate for a single flight. The main feature of our work is the use of empirical data collected at the Singapore Changi Airport, which drives the dynamic optimization model of a parallel queues system. We propose an event‐based dynamic programming model that simplifies considerably the optimization analysis even for large‐scale problems with 700+ booked passengers. We investigate the following research questions: (a) For a particular flight, what is the optimal number of counters the system should open with and what is the corresponding optimal total cost? (b) Given the state of the system at any event epoch, should we open another counter or not and what is the optimal cost‐to‐go from this state? The empirical data we collected at the airport are used to test the assumptions, estimate the key parameters, and run the computational experiments. We apply our model to 14 flights at the Singapore Changi Airport and identify cases in which, depending on the cost parameters, the model advocates the use of either a dynamic or a static policy. Although the model concerns only an exclusive‐use system, it is flexible enough to apply to other configurations such as a common‐use system or a single‐queue, multicounter system.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".