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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jaber Valizadeh & Alireza Zaki & Mohammad Movahed & Sasan Mazaheri & Hamidreza Talaei & Seyyed Mohammad Tabatabaei & Hadi Khorshidi & Uwe Aickelin, 2024. "An operational planning for emergency medical services considering the application of IoT," Operations Management Research, Springer, vol. 17(1), pages 267-290, March.
    • Handle: RePEc:spr:opmare:v:17:y:2024:i:1:d:10.1007_s12063-023-00423-7
    DOI: 10.1007/s12063-023-00423-7
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    References listed on IDEAS

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    1. Wu, Jiaming & Kulcsár, Balázs & Ahn, Soyoung & Qu, Xiaobo, 2020. "Emergency vehicle lane pre-clearing: From microscopic cooperation to routing decision making," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 223-239.
    2. Liu, Yang & Cui, Na & Zhang, Jianghua, 2019. "Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 1-16.
    3. Paul, Jomon A. & Wang, Xinfang (Jocelyn), 2019. "Robust location-allocation network design for earthquake preparedness," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 139-155.
    4. Yu, Chian-Son & Li, Han-Lin, 2000. "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 385-397, March.
    5. Mori, Masakatsu & Kobayashi, Ryoji & Samejima, Masaki & Komoda, Norihisa, 2017. "Risk-cost optimization for procurement planning in multi-tier supply chain by Pareto Local Search with relaxed acceptance criterion," European Journal of Operational Research, Elsevier, vol. 261(1), pages 88-96.
    6. Acuna, Jorge A. & Zayas-Castro, José L. & Charkhgard, Hadi, 2020. "Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    7. Kyohong Shin & Taesik Lee, 2020. "Emergency medical service resource allocation in a mass casualty incident by integrating patient prioritization and hospital selection problems," IISE Transactions, Taylor & Francis Journals, vol. 52(10), pages 1141-1155, October.
    8. Knight, V.A. & Harper, P.R. & Smith, L., 2012. "Ambulance allocation for maximal survival with heterogeneous outcome measures," Omega, Elsevier, vol. 40(6), pages 918-926.
    9. Sung, Inkyung & Lee, Taesik, 2016. "Optimal allocation of emergency medical resources in a mass casualty incident: Patient prioritization by column generation," European Journal of Operational Research, Elsevier, vol. 252(2), pages 623-634.
    10. John M. Mulvey & Robert J. Vanderbei & Stavros A. Zenios, 1995. "Robust Optimization of Large-Scale Systems," Operations Research, INFORMS, vol. 43(2), pages 264-281, April.
    11. Julián Alberto Espejo-Díaz & William J. Guerrero, 2021. "A multiagent approach to solving the dynamic postdisaster relief distribution problem," Operations Management Research, Springer, vol. 14(1), pages 177-193, June.
    12. Zhaosheng Yang & Huxing Zhou & Xueying Gao & Songnan Liu, 2013. "Multiobjective Model for Emergency Resources Allocation," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-6, February.
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