A Multi-Responder Cooperative Sweep Path Optimization Model for Building Emergency Evacuation

Guoyi Tu; Zhenming Fan; Zhixin Kang; Ruimeng Kang

doi:10.71222/j8qt9a78

Authors

Guoyi Tu Chengdu Shude High School (Guanghua Campus), Chengdu, China Author
Zhenming Fan Chengdu Shude High School (Guanghua Campus), Chengdu, China Author
Zhixin Kang Chengdu Shude High School (Guanghua Campus), Chengdu, China Author
Ruimeng Kang Chengdu Shude High School (Guanghua Campus), Chengdu, China Author

DOI:

https://doi.org/10.71222/j8qt9a78

Keywords:

emergency evacuation, path planning, dynamic optimization, risk assessment, resource allocation

Abstract

In modern high-rise buildings and complex architectural structures, efficient emergency evacuation remains a major logistical and safety challenge. This study develops a comprehensive mathematical optimization framework specifically designed for sweep-and-clear strategies, aiming to achieve full spatial coverage and ensure maximum occupant safety in minimal time. A core computational model utilizing a multi-source Bitmask-Dijkstra approach abstracts the building infrastructure as a complex graph network. It employs advanced bitmask encoding for tracking room-sweep states, thereby enabling optimal cooperative path planning among multiple rescue agents. Experimental validation in a single-floor office environment demonstrates that the system completes clearing operations in exactly 4.1 minutes while maintaining perfect coverage. For more complex and expansive environments, the integration of the Bitmask-A* algorithm with admissible heuristics significantly improves computational efficiency. When applied to multi-story daycare centers and large-scale warehouses, the model automatically optimizes responder allocation, deploying two responders for the daycare facility (achieving clearance in 11.4 minutes) and three for the warehouse (13.6 minutes). Furthermore, an extended model integrates highly realistic operational constraints, including communication network reliability, dynamic environmental hazards, dynamic risk prioritization, probabilistic occupant movement patterns, and responder load balancing. This captures real-world uncertainties effectively; for instance, under severe smoke conditions causing a 30% visibility reduction, the calculated clearing time realistically increases to 14.8 minutes. Ultimately, the primary contribution of this research is a systematic, highly scalable emergency path-planning methodology that successfully balances theoretical rigor with practical application, paving the way for intelligent emergency command systems.

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