An Improved Adaptive Parallel Genetic Algorithm for the Airport Gate Assignment Problem
Why this work is in the frame
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Bibliographic record
Abstract
Gate assignment problem (GAP) is the core issue of airport operation management. However, the limited resources of airport gates and the increase of flight scale result in serious problems for gate allocation. In this paper, to provide decision-making support for large-scale GAPs, a model based on gate assignment rules (e.g., flight type constraints, safe time interval constraints, and adjacency conflict constraints) is built to formulate the problem. An improved adaptive parallel genetic algorithm (APGA) is then designed to solve the model. The algorithm is effective because it introduces the idea of elite strategy and parallel design and can adaptively adjust the crossover probability. Moreover, different instances are presented to demonstrate the proposed algorithm. The calculation results of this algorithm are compared with those of standard genetic algorithm and CPLEX, which show that the proposed algorithm has better performance and takes a shorter computational time. In addition, we verify the stability and practicability of the algorithm by repeated experiments on large-scale flight data.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it