A novel Hybrid PSO-Heuristic Algorithm with Combinatorial Benders' Cuts for maximal evacuation planning in wildfire disasters
Bibliographic record
Abstract
Natural disasters, such as floods and fires, affect various regions of the world every year. One of the most critical aspects of disaster management and planning is facilitating the evacuation of people. Therefore, this study develops a mathematical model for emergency evacuation, taking into account the constraints and limitations of transferring individuals to shelters. The model, called the maximal bus evacuation planning, considers constraints including road blockages, vehicle fuel, and passenger capacities. Given the Nondeterministic Polynomial-time hard complexity of this problem, a Hybrid Particle Swarm Optimization–Heuristic Algorithm, which combines the Particle Swarm Optimization Algorithm with a Heuristic Algorithm , is used to solve the nonlinear model. Furthermore, we linearize the model and solve it using the developed Combinatorial Benders' Cuts approach in three versions. Numerical computations are examined under various scenarios, and the outputs of the Hybrid Particle Swarm Optimization – Heuristic algorithm reach suitable solutions within a short timeframe. Additionally, the results from solving the linearized model using the Combinatorial Benders' Cuts show that, in most cases, the execution time of the second version is better than the other versions.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".