Optimal allocation of defibrillator drones in mountainous regions

Christian Wankmüller (), Christian Truden (), Christopher Korzen (), Philipp Hungerländer (), Ewald Kolesnik () and Gerald Reiner ()
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Christian Wankmüller: Alpen-Adria-Universität Klagenfurt
Christian Truden: Alpen-Adria Universität Klagenfurt
Christopher Korzen: Alpen-Adria-Universität Klagenfurt
Philipp Hungerländer: Alpen-Adria Universität Klagenfurt
Ewald Kolesnik: Medical University of Graz
Gerald Reiner: Vienna University of Economics and Business

OR Spectrum: Quantitative Approaches in Management, 2020, vol. 42, issue 3, No 8, 785-814

Abstract: 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 programming (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s00291-020-00575-z

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