Enhancing Best-First Search Algorithms for Aircraft Ground Trajectory Optimization With Non-Additive Cost Functions
Bibliographic record
Abstract
This paper presents a study conducted at the Laboratory of Applied Research in Active Controls, Avionics, and AeroServoElasticity (LARCASE) for optimizing aircraft ground trajectories at airports using non-additive cost functions. For this purpose, the airport was modeled as an undirected graph composed of nodes and edges, with edge properties varying depending on the aircraft path. Consequently, the value of the cost function can not be determined by summing the cost of individual edges, but it also depends on the overall aircraft path. To address this challenge, a modified bi-directional A* algorithm was developed to find the optimal path between two nodes in the graph that minimizes fuel consumption or taxiing time, while satisfying geometrical constraints. The cost function used in the optimization algorithm was defined as non-additive, making the proposed optimization algorithm suitable for solving more complex problems where traditional Best-First Search (BFS) methods, such as Dijkstra or A* algorithms, fail to provide optimal solutions (considering non-additive cost functions). The proposed algorithm was tested at Montreal (CYUL) Airport using a fuel consumption model of the Cessna Citation X business jet aircraft derived from a Level-D Research Aircraft Flight Simulator (RAFS). The results showed that it was possible to reduce the aircraft fuel consumption by an average of 3.86% and the taxiing time by an average of 4.10%.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".