A Constraint Optimization Approach for the Allocation of Multiple Search Units in Search and Rescue Operations
Bibliographic record
Abstract
Search and Rescue (SAR) comprises the search for and provision of aid to persons who are, or who are feared to be, in distress or in imminent danger of loss of life. Time is a crucial factor for survivors who must be found quickly and search planning may get complex in the case of a large search area and multiple search resources. The problem we address in this paper is that of defining and assigning multiple non-overlapping rectangular sub-areas to search units (search aircraft) such that the search plan is operationally feasible and the total probability of success is maximized. We present algorithms we developed for the search resources allocation problem for aeronautical SAR incidents when multiple indivisible searchers are present. These algorithms are based on classical search theory and on constraint programming. We assume that the search effort is continuous and measured by track length, that the search object is stationary and that search is conducted in discrete space. We present experimental results for a realistic SAR case overland.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".