Multi-objective evolutionary optimization of a military air transportation fleet mix with the flexibility objective
Why this work is in the frame
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Bibliographic record
Abstract
The Non-dominated Sorting Genetic Algorithm-II is applied to a multi-objective air transportation fleet-mix problem for finding flexible fleet mixes. The Stochastic Fleet Estimation model, which is Monte Carlo-based, is used to determine average annual requirements that a fleet must meet. We search for Pareto-optimal combinations of platform-to-task assignments that can be used to complete stochastically generated scenarios. Solutions are evaluated using three objectives, with a goal of maximizing flexibility in accomplishing each task within its closure time, and minimizing fleet cost and total task duration. Optimization over all three objectives found very flexible low cost fleets, which were not discovered using previous two-objective and three-objective optimizations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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