A Constraint Optimization Approach for the Allocation of Multiple Search Units in Search and Rescue Operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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