A Tabu Search-Based Algorithm for Airport Gate Assignment: A Case Study in Kunming, China
Bibliographic record
Abstract
An airport gate is the core resource of an airport operation, which is an important place for passengers to get on and off the aircraft and for maintaining aircraft. It is the prerequisite for other related dispatch. Effective and reasonable allocation of gates can reduce airport operating costs and increase passenger satisfaction. Therefore, an airport gate assignment problem (AGAP) needs to be urgently solved in the actual operation of the airport. In this paper, considering the actual operation of the airport, we formulate an integer programming model for AGAP by considering multiple constraints. The model aims to maximize the number of passengers on flights parked at the gate. A tabu search-based algorithm is designed to solve the problem. In the process of algorithm design, an effective initial solution is obtained. A unique neighborhood structure and search strategy for tabu search are designed. The algorithm can adapt to the dynamic scheduling of airports. Finally, tests are performed using actual airport data selected from Kunming Changshui International Airport in China. The experimental results indicate that the proposed method can enhance the local search ability and global search ability and get satisfactory results in a limited time. These results provide an effective support for the actual gate assignment in airport operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".