An approximate dynamic programming approach to tackling mass evacuation operations
Bibliographic record
Abstract
This article examines a major maritime disaster scenario in which the objective is to maximize the number of survivors. To achieve this objective, we seek to optimize the decision policy to load individuals onto a helicopter for transport from an evacuation site to a forward operating base. Our contributions are twofold. First, we formulate the loading problem as a finite-horizon Markov Decision Process. Second, since the curse of dimensionality renders exact methods not applicable, we use an Approximate Dynamic Programming (ADP) approach to explore the efficacy of these methods to the problem. We generate three ADP policies, each using a value function approximation based on a unique post-decision state variable and a lookup table representation. We compare these ADP policies to a random policy, a myopic policy, and Policy Function Approximation (PFA) which evacuates survivors in order of triage category starting with the most critically ill. Our results show that an ADP-generated policy which prioritizes the evacuation of healthy individuals improved performance by 34 ± 5% versus the random policy, 15 ± 3% versus the myopic policy, and 42 ± 3% versus the PFA policy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".