A Multi-Objective Simulation-Based Optimization Framework for Multi-Agent Phased Evacuation Strategies in Fire *
Bibliographic record
Abstract
This study proposes a multi-agent-based simulation and optimization framework to enhance phased evacuation strategies by integrating fire dynamics, evacuees’ characteristics, and evacuation processes. Unlike conventional models, this framework probabilistically assesses the integrated effects of heat, asphyxiant gases, and irritant gases on different evacuees’ incapacitation. The Non-dominated Sorting Genetic Algorithm III (NSGA-III), coupled with a trained neural network, is employed to find optimal phased evacuation strategies considering the Total Evacuation Time (TET), congestion, and fire impact. To assess its effectiveness, the framework is applied to a fire scenario in an educational building, comparing simultaneous and phased evacuation strategies. Results demonstrate that the selected phased evacuation strategy significantly enhances evacuation efficiency, reducing TET, congestion, and fire impact by 14.8%, 33.3%, and 13.1%, respectively. These findings underscore the framework’s potential for improving fire evacuation planning, providing a simulation-based approach to optimizing evacuation strategies and enhancing safety in fire.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 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".