A robust possibilistic programming approach to multiperiod hospital evacuation planning problem under uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In this paper, a biobjective programming model is developed to address the hospital evacuation problem under uncertainty. It aims to concurrently minimize the total evacuation time and the total weighted number of unevacuated patients in each period. The presented model considers two types of patients and three transportation modes. Moreover, the evacuating hospitals are divided into two groups. In the first group, it is not possible to send vehicles to the evacuating hospitals due to the poor road condition or congestion, whereas there is no such limitation in the second group. A robust possibilistic programming approach is adopted to deal with the inherent uncertainty in the input data. To cope with the computational complexity of the problem, two well‐known metaheuristic algorithms are developed to solve the large‐sized problems. Finally, several computational experiments and sensitivity analyses are conducted and the results are analyzed.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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