Multiobjective evolutionary optimization of a transportation fleet with a modified monetary cost function
Why this work is in the frame
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Bibliographic record
Abstract
We propose a modified monetary cost function for civilian transportation applications such as couriering cargo, or transporting passengers using a commercial airline. We apply the non-dominated sorting genetic algorithm-II (NSGA-II) to perform a multiobjective optimization of the Stochastic Fleet Estimation (SaFE) model. SaFE is a Monte Carlo-based model which generates a vehicle fleet based on the set of requirements that the fleet is supposed to meet. We search for Pareto-optimal combinations of valid platform-assignments for a list of tasks, which can be applied to complete scenarios output by SaFE. Solutions are evaluated on two objectives, with the goal of minimizing fleet cost, and total task duration time (a performance measure). We present optimization results and describe differences between current results and past research done on a related military problem. Finally, we show why using the risk formulation of not being able to accomplish future scenarios for military applications does not make sense for civilian applications.
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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 | 0.002 |
| 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