Master data

Title: Optimal allocation of defibrillator drones in mountainous regions
Subtitle:
Abstract:

Responding to emergencies in Alpine terrain is quite challenging as air ambulances and mountain rescue services are often confronted with logistics challenges and adverse weather conditions that extend the response times required to provide life-saving support. Among other medical emergencies, sudden cardiac arrest (SCA) is the most time-sensitive event that requires the quick provision of medical treatment including cardiopulmonary resuscitation and electric shocks by automated external defibrillators (AED). An emerging technology called unmanned aerial vehicles (or drones) is regarded to support mountain rescuers in overcoming the time criticality of these emergencies by reducing the time span between SCA and early defibrillation. A drone that is equipped with a portable AED can fly from a base station to the patient’s site where a bystander receives it and starts treatment. This paper considers such a response system and proposes an integer linear program to determine the optimal allocation of drone base stations in a given geographical region. In detail, the developed model follows the objectives to minimize the number of used drones and to minimize the average travel times of defibrillator drones responding to SCA patients. In an example of application, under consideration of historical helicopter response times, the authors test the developed model and demonstrate the capability of drones to speed up the delivery of AEDs to SCA patients. Results indicate that time spans between SCA and early defibrillation can be reduced by the optimal allocation of drone base stations in a given geographical region, thus increasing the survival rate of SCA patients.

Keywords: Drone; Emergency response, Sudden cardiac arrest, Automated external defibrillator, Location allocation, Integer linear programmin
Publication type: Article in journal (Authorship)
Publication date: 10.02.2020 (Online)
Published by: OR Spectrum
OR Spectrum
to publication
 ( Springer Verlag GmbH; )
Title of the series: -
Volume number: -
Issue: -
First publication: Yes
Version: -
Page: pp. 1 - 30

Versionen

Keine Version vorhanden
Publication date: 10.02.2020
ISBN (e-book): -
eISSN: -
DOI: http://dx.doi.org/10.1007/s00291-020-00575-z
Homepage: https://link.springer.com/article/10.1007/s00291-020-00575-z
Open access
  • Available online (open access)

Assignment

Organisation Address
Fakultät für Wirtschafts- und Rechtswissenschaften
 
Institut für Produktions-, Energie- und Umweltmanagement
 
Abteilung für Produktionsmanagement und Logistik
Universitätsstr. 65-67
A-9020 Klagenfurt
Austria
To organisation
Universitätsstr. 65-67
AT - A-9020  Klagenfurt
Fakultät für Technische Wissenschaften
 
Institut für Mathematik
Universitätsstraße 65-67
9020 Klagenfurt am Wörthersee
Austria
   math@aau.at
https://www.aau.at/mathematik
To organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Categorisation

Subject areas
  • 502 - Economics
Research Cluster No research Research Cluster selected
Citation index
  • Science Citation Index (SCI)
Information about the citation index: Master Journal List
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: n.a.)
Classification raster of the assigned organisational units:
working groups No working group selected

Cooperations

Organisation Address
Medizinische Universität Graz
Auenbruggerplatz 2
8036 Graz
Austria - Styria
Auenbruggerplatz 2
AT - 8036  Graz

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