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Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm

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  • Qing Han

    (Intelligent Systems & Networks Group, EEE, Imperial College, London SW7 2BT, UK)

Abstract

Emergency rescues require that first responders provide support to evacuate injured and other civilians who are obstructed by the hazards. In this case, the emergency personnel can take actions strategically in order to rescue people maximally, efficiently and quickly. The paper studies the effectiveness of a random neural network (RNN)-based task assignment algorithm involving optimally matching emergency personnel and injured civilians, so that the emergency personnel can aid trapped people to move towards evacuation exits in real-time. The evaluations are run on a decision support evacuation system using the Distributed Building Evacuation Simulator (DBES) multi-agent platform in various emergency scenarios. The simulation results indicate that the RNN-based task assignment algorithm provides a near-optimal solution to resource allocation problems, which avoids resource wastage and improves the efficiency of the emergency rescue process.

Suggested Citation

  • Qing Han, 2013. "Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm," Future Internet, MDPI, vol. 5(4), pages 1-20, October.
    • Handle: RePEc:gam:jftint:v:5:y:2013:i:4:p:515-534:d:29596
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    References listed on IDEAS

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    1. Pentico, David W., 2007. "Assignment problems: A golden anniversary survey," European Journal of Operational Research, Elsevier, vol. 176(2), pages 774-793, January.
    2. Antoine Desmet & Erol Gelenbe, 2013. "Graph and Analytical Models for Emergency Evacuation," Future Internet, MDPI, vol. 5(1), pages 1-10, February.
    3. Gelenbe, Erol & Cao, Yonghuan, 1998. "Autonomous search for mines," European Journal of Operational Research, Elsevier, vol. 108(2), pages 319-333, July.
    4. MacGregor Smith, J., 1991. "State-dependent queueing models in emergency evacuation networks," Transportation Research Part B: Methodological, Elsevier, vol. 25(6), pages 373-389, December.
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    Cited by:

    1. Georgios Fragkos & Pavlos Athanasios Apostolopoulos & Eirini Eleni Tsiropoulou, 2019. "ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems," Future Internet, MDPI, vol. 11(1), pages 1-17, January.

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