Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm

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 effectiv...

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Bibliographic Details
Main Author:	Qing Han
Format:	Article
Language:	English
Published:	MDPI AG 2013-10-01
Series:	Future Internet
Subjects:	rescuers random neural network (RNN) task assignment
Online Access:	http://www.mdpi.com/1999-5903/5/4/515

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spelling	doaj-1bc927dda1444df9a913af5a45b04f1d2020-11-25T00:56:31ZengMDPI AGFuture Internet1999-59032013-10-015451553410.3390/fi5040515Managing Emergencies Optimally Using a Random Neural Network-Based AlgorithmQing HanEmergency 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.http://www.mdpi.com/1999-5903/5/4/515rescuersrandom neural network (RNN)task assignment
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Qing Han
spellingShingle	Qing Han Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm Future Internet rescuers random neural network (RNN) task assignment
author_facet	Qing Han
author_sort	Qing Han
title	Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm
title_short	Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm
title_full	Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm
title_fullStr	Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm
title_full_unstemmed	Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm
title_sort	managing emergencies optimally using a random neural network-based algorithm
publisher	MDPI AG
series	Future Internet
issn	1999-5903
publishDate	2013-10-01
description	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.
topic	rescuers random neural network (RNN) task assignment
url	http://www.mdpi.com/1999-5903/5/4/515
work_keys_str_mv	AT qinghan managingemergenciesoptimallyusingarandomneuralnetworkbasedalgorithm
_version_	1725226806835937280

Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm

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