Aug 2026· Journal of Frontiers in Technology and Engineering Integration· Vol 2· 0 citations· 13 references
Abstract
In the critical domain of emergency response, rapid building evacuation and rescue are often hindered by complex layouts and dynamic hazards such as fire spread, toxic smoke, structural instability, and unpredictable occupant behaviour. To address the challenge of optimizing rescue sweep strategies, this paper establishes a comprehensive mathematical framework that transitions from traditional static planning to dynamic, adaptive decision-making, with the objectives of minimizing rescue completion time and maximizing occupant safety under uncertain conditions. First, a three-level evaluation system based on the AHP–Entropy Weight Method is constructed to quantify 32 critical influence factors, ranging from environmental conditions and building layout to occupant behaviour and technical support. The resulting weighted framework provides a data-driven foundation for subsequent optimization models. Three representative building configurations are then examined. For a 1D linear layout, a Divide-and-Conquer strategy reduces completion time by 42% compared with a single-entry approach. For a 2D branching layout, the rescue problem is formulated as a load-balanced Multi-Agent Traveling Salesman Problem (MA-TSP). For a 3D high-rise structure, a hierarchical Pincer Strategy is developed by nesting the MA-TSP within a multi-objective vehicle-routing framework and strategically leveraging vertical assets such as fire-service elevators. Finally, a Dynamic Adaptive Replanning Model is introduced using time-dependent hazard functions, dynamic edge costs, and occupant re-entry events. A Rolling-Horizon Replanning mechanism continuously updates rescue routes as the emergency evolves.
A critical gap in disaster response persists between optimized logistical plans and the complex, often unpredictable evacuation behaviors of affected populations. Traditional location–allocation models often prove insufficient in practice, as they typically treat evacuees as homogeneous, rational actors, thereby overlooking the dynamic, hierarchical needs that govern decision‐making on the ground. This study addresses this gap by introducing a hybrid optimization framework that systematically translates data‐driven behavioral insights into a multi‐objective model for shelter location and material assignment. Rather than modeling microscopic routing behaviors, our methodological innovation lies in a two‐stage pipeline. First, we integrate thematic analysis of large‐scale social media data (3400 relevant posts) from recent earthquakes to extract and quantify time‐varying demand patterns for three core needs: essential survival, medical treatment, and psychological needs. Second, these empirically derived priority dynamics are embedded as dynamic parameters within the optimization model. Validated against the 2013 Ya'an earthquake case study and compared with classical baselines (e.g., the p‐median model), the framework reduces the average evacuation distance while ensuring feasible access for heterogeneous vulnerable groups. Concurrently, it enhances resource equity by dynamically prioritizing material allocation based on the evolving urgency of evacuee needs. This research thus contributes not only a specific decision‐support tool but also a generalizable paradigm for bridging descriptive behavioral data with prescriptive operations models, offering a more realistic foundation for risk analysis in humanitarian logistics.
Evacuation planning for disaster preparedness requires making critical decisions under uncertainty before the number and spatial distribution of evacuees are known, including shelter location, evacuation route assignment, and relief supply prepositioning. Because these decisions are highly interdependent, planners must balance the competing objectives of maximizing relief demand coverage and minimizing evacuation time. We propose, to our knowledge, the first adaptive robust evacuation planning model to jointly optimize shelter locations, evacuation route assignments, relief supply prepositioning, and post-disaster relief item distribution. The model minimizes the worst-case weighted sum of unmet demand for relief items across shelters and the congestion-dependent evacuation time. We characterize theoretical complexity drivers of the resulting problem with mixed-integer recourse and develop a partition-and-bound algorithm that maintains tractability by selectively partitioning only the most critical subpartition of the uncertainty set while producing strong upper and lower bounds. To quantify the value of centralized route planning, we also formulate a user route choice alternative in which evacuees choose among acceptable routes. Computational experiments quantify the value of centralized route planning, which reduces worst-case unmet demand and evacuation time by up to 90.6\% and 79.3\%, respectively, relative to decentralized user route choice. Adaptive post-disaster supply redistribution further improves relief demand coverage. Coordination between evacuation routing and relief distribution creates substantial operational value under uncertainty. Centralized route planning primarily mitigates congestion by coordinating evacuee flows across shelters, whereas adaptive redistribution primarily improves relief demand coverage when relief supplies are scarce or inflexibly prepositioned.
Jaehyuk Kim, Merve Bodur, Maria E. Mayorga et al.· 0 citations
Existing methods for optimizing urban flood emergency evacuation routes often fail to account for the dynamic evolution of inundation and the heterogeneity of evacuees' ages. To address this gap, this paper proposes a personalized evacuation route optimization approach that integrates Agent-Based Modeling (ABM) with Deep Q-Network (DQN) reinforcement learning. The proposed method incorporates water-depth-dependent speed reduction models and instability thresholds specific to children, adults, and the elderly into both the state space and reward function, enabling DQN to iteratively learn optimal routes that balance safety and efficiency. Using the "7·20" extreme rainstorm in Zhengzhou as a case study, we constructed a 100-year return-period pluvial flooding scenario and conducted evacuation simulations under three activation timings-0, 1, and 2 hours after rainfall onset-comparing the proposed method against the traditional A* algorithm. Results demonstrate that the experimental group reduced average evacuation time by 17.8%-42.1% relative to the control group, with particularly pronounced improvements for children and the elderly (up to 45.1%). The safety metric F-value in the experimental group exceeded that of the control group by more than 0.3 under deep-water conditions and exhibited a more gradual decline. These findings confirm that the DQN-based approach, which integrates age heterogeneity and dynamic water depth, substantially enhances both the timeliness and safety of emergency evacuation during urban floods, offering more reliable route planning for high-risk populations and providing a scientific basis for optimizing urban flood emergency response protocols.
Yi Wang, Liang Mao· Academic Journal of Manageme...· 0 citations
This study examines human evacuation performance in complex building environments under highly uncertain fire conditions, with a focus on how spatial knowledge and decision-making strategies influence exposure to hazardous conditions during emergencies in smart building contexts. A hybrid simulation framework integrating physical fire modeling, agent-based simulation (ABM), and data-driven analysis is applied to a large academic building in Santiago, Chile, incorporating heterogeneous occupant profiles with different levels of spatial knowledge while evaluating key environmental variables such as temperature, oxygen (O2) concentration, carbon dioxide (CO2) levels, and carbon monoxide (CO) concentrations. Stochastic behavioral variability is captured through Monte Carlo simulation, and associations between environmental conditions and evacuation responses are analyzed using Kendall’s correlation. Results show that evacuation strategies based on optimal route knowledge significantly reduce exposure to life-threatening conditions and decrease evacuation times by 34% for adults and 8.5% for young occupants compared with scenarios without prior spatial information. These findings highlight the critical role of spatial knowledge in evacuation decision-making and provide a transferable methodological framework for improving smart building safety systems and data-driven evacuation planning in high-occupancy urban environments.
R. Ternero, M. Alfaro, Gabriel Larrain et al.· Smart Cities· 0 citations
High-rise buildings present critical fire safety challenges due to their vertical configuration, high occupant density, and complex evacuation dynamics. Fire events in such structures can escalate rapidly, with smoke spread and compromised egress routes significantly increasing risks to life and assets. Conventional fire risk assessment and evacuation strategies are largely static and pre-incident focused, limiting their effectiveness under evolving fire conditions. This paper applies Bow-Tie Analysis (BTA) to systematically identify fire hazards, escalation pathways, and key preventive and mitigative barriers in high-rise buildings, and introduces a novel framework termed Dynamic Bow-Tie Integration with Smart Evacuation Systems (DBT-SES). The proposed approach transforms traditional BTA into a real-time, adaptive risk management tool through integration of IoT-based sensing, AI-driven fire and smoke propagation modelling, and dynamic evacuation algorithms. A representative high-rise fire scenario is analysed to compare static evacuation planning with the DBT-SES framework. Results indicate improved evacuation efficiency, reduced congestion, lower occupant exposure to hazardous conditions, and enhanced barrier effectiveness under dynamic conditions. The study demonstrates the value of intelligent, data-driven barrier management and provides practical insights for improving fire risk management and emergency response in high-rise structures.
D. Abia, D. Kalu, M. Iwegbu et al.· SPE Nigeria Annual Internati...· 0 citations
The rapidly growing tourism industry has brought forth substantial safety concerns, particularly in the context of emergency evacuations necessitated by natural and human-induced disasters. Tourists often lack the necessary orientation, information, and preparedness, rendering them vulnerable during such crises. While research has extensively explored tourist behavior and evacuation procedures independently, the intersection of these two fields remains underexamined. This study introduces a simulation model utilizing MATSim to characterize the behaviors of tourists during emergencies, highlighting navigation errors and decision-making processes. Two novel routers - “Random Walk” and “Landmark Assisted” - have been developed to better reflect tourist navigation challenges. A case study in Conegliano, Italy, demonstrates the effectiveness of these routers under two evacuation policies: predefined and undefined destinations. Results indicate significant disparities in evacuation times: optimal routes average 50 minutes, while random navigation extends this to 544 minutes. The Landmark Assisted router improves evacuation to 73 minutes, underscoring the importance of identifiable landmarks. Additionally, managing intersections further reduces evacuation times. This simulation framework serves as a decision-making tool for evaluating evacuation policies, providing insights into optimizing resource allocation and enhancing overall efficacy in emergency scenarios. Future research should focus on developing optimization algorithms for intersection management selection, reinforcing the practical applicability of this model in real-world contexts.
Oren E. Nahum, Omri Mayost· International Journal of Ind...· 0 citations
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