An operational planning for emergency medical services considering the application of IoT

Jaber Valizadeh (), Alireza Zaki (), Mohammad Movahed (), Sasan Mazaheri (), Hamidreza Talaei (), Seyyed Mohammad Tabatabaei (), Hadi Khorshidi () and Uwe Aickelin ()
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Jaber Valizadeh: Islamic Azad University, Saveh Branch
Alireza Zaki: University of Tehran
Mohammad Movahed: Valdosta State University
Sasan Mazaheri: Shahid Beheshti University
Hamidreza Talaei: Arak University
Seyyed Mohammad Tabatabaei: Darolelm Yazd Institute of Higher Education
Hadi Khorshidi: The University of Melbourne
Uwe Aickelin: The University of Melbourne

Operations Management Research, 2024, vol. 17, issue 1, No 15, 267-290

Abstract: Abstract In the last two years, the worldwide outbreak of the COVID-19 pandemic and the resulting heavy casualties have highlighted the importance of further research in healthcare. In addition, the advent of new technologies such as the Internet of Things (IoT) and their applications in preventing and detecting casualty cases has attracted a lot of attention. The IoT is able to help organize medical services by collecting significant amounts of data and information. This paper proposes a novel mathematical model for Emergency Medical Services (EMS) using the IoT. The proposed model is designed in two phases. In the first phase, the data is collected by the IoT, and the demands for ambulances are categorized and prioritized. Then in the second phase, ambulances are allocated to demand areas (patients). Two main objectives of the proposed model are reducing total costs and the mortality risk due to lack of timely service. In addition, demand uncertainty for ambulances is considered with various scenarios at demand levels. Numerical experiments have been conducted on actual data from a case study in Kermanshah, Iran. Due to the NP-hard nature of the mathematical model, three meta-heuristic algorithms Multi-Objective Simulated Annealing (MOSA) algorithm and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, and L-MOPSO have been used to solve the proposed model on medium and large scales in addition to the exact solution method. The results show that the proposed model significantly reduces mortality risk, in addition to reducing total cost. Data analysis also led to useful managerial insights.

Keywords: Robust; Risk; Mathematical Modeling; Emergency Medical Services; Internet-of-Things (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12063-023-00423-7

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