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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".