Improved Ant Colony Optimization for the Operational Aircraft Maintenance Routing Problem with Cruise Speed Control
Why this work is in the frame
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Bibliographic record
Abstract
The operational aircraft maintenance routing problem (OAMRP) plays a critical part in producing considerable cost reductions for airlines, since its solution directly influences the number of operating leased aircraft. To reduce the quantity of required aircraft, adopting cruise speed control in OAMRP is a good strategy. In this paper, we investigate the OAMRP with cruise speed control. The objective is to minimize the required quantity of aircraft by finding the optimal aircraft routes through cruise time optimization. The focus is on solving two issues simultaneously: (i) optimization of cruise times and (ii) determination of aircraft routes. Since the combination of two intricate sets of decisions poses significant methodological challenges, the difficulty lies in how to efficiently solve it. Accordingly, the goal of this study is twofold: (i) to design a preprocessing step to reduce the network size and (ii) to develop an improved ant colony optimization (IACO) algorithm with a new state transition mechanism to provide the guidance for cruise times optimization and a new pheromone updating mechanism to enhance the search efficiency and precision. Using data from the Bureau of Transportation Statistics (BTS), we demonstrate the computational efficiency of the preprocessing step and the IACO algorithm.
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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 | 0.001 | 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