Optimization-Based Planning of Emergency Building Sweeps Using a Multi-Agent Vehicle Routing Model
DOI:
https://doi.org/10.62051/eqavsa03Keywords:
Evacuation planning; Vehicle Routing Problem; Emergency response; Multi-agent optimization; Building sweep.Abstract
In emergencies, every second matters. First responders must ensure that no occupant is left behind while operating under conditions of limited visibility, increasing danger, and incomplete information. Well-designed evacuation plans reduce the risk to responders and improve the effectiveness of their efforts. In particular, efficient sweeping routes and techniques can be the difference between a safe evacuation and a preventable tragedy. To support emergency planning, we develop a rigorous model for evacuation sweeps those accounts for realistic building layouts and operational constraints. The sweep problem is formulated as a multi-agent routing and scheduling task, modeled as a Vehicle Routing Problem with service times and coordinated routes. Rooms are treated as mandatory service nodes, hallways and stairwells as weighted travel edges, and responders as routing agents. A mixed-integer optimization framework propagates travel and service times, while a makespan-minimization objective determines the shortest feasible complete-building sweep. The model is tested on a one-story office building and on multi-floor school and hospital layouts with varying room types, hazard patterns, and responder counts. Extensions incorporate factors such as hazard spread, communication delays, room priority, and occupant movement. Results show that the VRP-based approach is flexible, scalable, and effective for planning safe and efficient building sweeps, allowing responders to focus on execution rather than on-site planning.
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References
[1] Ben-Tal, A., & Nemirovski, A. (2001). Robust optimization methodology. Mathematical Programming.
[2] Brotcorne, L., Laporte, G., & Semet, F. (2003). Dynamic vehicle routing for emergency services. European Journal of Operational Research.
[3] Chalmet, L., Francis, R., & Saunders, P. (1982). Network models for building evacuation. Management Science.
[4] Church, R., & ReVelle, C. (2002). The maximal covering location problem. Papers in Regional Science.
[5] Cox, L. (2012). Risk analysis: Foundations, models, and methods. Springer.
[6] Dantzig, G., & Ramser, J. (1959). The truck dispatching problem. Management Science.
[7] Drabek, T. (1996). Human responses to disaster. Springer.
[8] Finney, M. (1998). FARSITE: Fire Area Simulator. USDA Forest Service.
[9] Golden, B., Raghavan, S., & Wasil, E. (2008). The vehicle routing problem: Latest advances. Springer.
[10] Gwynne, S., Galea, E., & Lawrence, P. (1999). A review of the methodologies used in evacuation modelling. Fire and Materials.
[11] Helbing, D., Farkas, I., & Vicsek, T. (2000). Simulating dynamical features of escape panic. Nature.
[12] Kuligowski, E. (2013). Predicting human behavior during fires. Fire Technology.
[13] Laporte, G. (2009). Fifty years of vehicle routing. Transportation Science.
[14] McGrattan, K., et al. (2013). Fire Dynamics Simulator Technical Reference Guide. NIST.
[15] Pan, X., Han, C., Dauber, K., & Law, K. (2006). A multi-agent based framework for evacuation simulation. Simulation.
[16] Perron, L., & Furnon, V. (2019). OR-Tools: Google Optimization Tools. Google Research.
[17] Ronchi, E., & Nilsson, D. (2013). Evacuation modelling trends. Fire Technology.
[18] Sheffi, Y. (2005). The humanitarian logistics system. MIT Press.
[19] Toth, P., & Vigo, D. (2014). Vehicle routing: Problems, methods, and applications. SIAM.
[20] Yi, W., & Kumar, A. (2007). Ant colony optimization for disaster relief logistics. Transportation Research Part E.
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