Dispatch guided allocation optimization for effective emergency response

Effective emergency (medical, fire or criminal) response iscrucial for improving safety and security in urban environments. Recent research in improving effectiveness of emergency management systems (EMSs) has utilized data-drivenoptimization models for efficient allocation of emergency response veh...

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Main Authors: GHOSH, Supriyo, VARAKANTHAM, Pradeep
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/4306
https://ink.library.smu.edu.sg/context/sis_research/article/5309/viewcontent/17208_76518_1_PB.pdf
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spelling sg-smu-ink.sis_research-53092019-02-21T08:30:21Z Dispatch guided allocation optimization for effective emergency response GHOSH, Supriyo VARAKANTHAM, Pradeep Effective emergency (medical, fire or criminal) response iscrucial for improving safety and security in urban environments. Recent research in improving effectiveness of emergency management systems (EMSs) has utilized data-drivenoptimization models for efficient allocation of emergency response vehicles (ERVs) to base locations. However, thesedata-driven optimization models either ignore the dispatchstrategy of ERVs (typically the nearest available ERV is dispatched to serve an incident) or employ myopic approaches(e.g., greedy approach based on marginal gain). This resultsin allocations that are not synchronised with the real evolution dynamics on the ground or can be improved significantly.To bridge this gap, we make the following contributions: (1)We first provide a novel exact optimization model for allocation of ERVs that incorporates the non-linear real-worlddispatch strategy as linear constraints and ensures that optimization exactly imitates the real-world dynamics of EMS;(2) In order to improve scalability, we then provide two novelheuristic approaches to solve problems with large number ofemergency incidents; and (3) Finally, using two real-worldEMS data sets, we empirically demonstrate that our heuristic approaches provide significant improvement over the bestknown benchmark approach. 2018-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4306 https://ink.library.smu.edu.sg/context/sis_research/article/5309/viewcontent/17208_76518_1_PB.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Emergency response Constraint optimization Heuristics Data-driven modelling Computer Sciences Medicine and Health Sciences Operations Research, Systems Engineering and Industrial Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Emergency response
Constraint optimization
Heuristics
Data-driven modelling
Computer Sciences
Medicine and Health Sciences
Operations Research, Systems Engineering and Industrial Engineering
spellingShingle Emergency response
Constraint optimization
Heuristics
Data-driven modelling
Computer Sciences
Medicine and Health Sciences
Operations Research, Systems Engineering and Industrial Engineering
GHOSH, Supriyo
VARAKANTHAM, Pradeep
Dispatch guided allocation optimization for effective emergency response
description Effective emergency (medical, fire or criminal) response iscrucial for improving safety and security in urban environments. Recent research in improving effectiveness of emergency management systems (EMSs) has utilized data-drivenoptimization models for efficient allocation of emergency response vehicles (ERVs) to base locations. However, thesedata-driven optimization models either ignore the dispatchstrategy of ERVs (typically the nearest available ERV is dispatched to serve an incident) or employ myopic approaches(e.g., greedy approach based on marginal gain). This resultsin allocations that are not synchronised with the real evolution dynamics on the ground or can be improved significantly.To bridge this gap, we make the following contributions: (1)We first provide a novel exact optimization model for allocation of ERVs that incorporates the non-linear real-worlddispatch strategy as linear constraints and ensures that optimization exactly imitates the real-world dynamics of EMS;(2) In order to improve scalability, we then provide two novelheuristic approaches to solve problems with large number ofemergency incidents; and (3) Finally, using two real-worldEMS data sets, we empirically demonstrate that our heuristic approaches provide significant improvement over the bestknown benchmark approach.
format text
author GHOSH, Supriyo
VARAKANTHAM, Pradeep
author_facet GHOSH, Supriyo
VARAKANTHAM, Pradeep
author_sort GHOSH, Supriyo
title Dispatch guided allocation optimization for effective emergency response
title_short Dispatch guided allocation optimization for effective emergency response
title_full Dispatch guided allocation optimization for effective emergency response
title_fullStr Dispatch guided allocation optimization for effective emergency response
title_full_unstemmed Dispatch guided allocation optimization for effective emergency response
title_sort dispatch guided allocation optimization for effective emergency response
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/4306
https://ink.library.smu.edu.sg/context/sis_research/article/5309/viewcontent/17208_76518_1_PB.pdf
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