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An Agent-Based Architecture of the Digital Twin for an Emergency Department

Author

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  • Thierry Moyaux

    (University Lyon, INSA Lyon, University Jean Monnet Saint-Etienne, University Claude Bernard Lyon 1, University Lumière·Lyon 2, DISP UR4570, 69621 Villeurbanne, France
    These authors contributed equally to this work.)

  • Yinling Liu

    (University Lorraine, CNRS, CRAN UMR 7039, University Lorraine, 54000 Nancy, France
    These authors contributed equally to this work.)

  • Guillaume Bouleux

    (University Lyon, INSA Lyon, University Jean Monnet Saint-Etienne, University Claude Bernard Lyon 1, University Lumière·Lyon 2, DISP UR4570, 69621 Villeurbanne, France
    These authors contributed equally to this work.)

  • Vincent Cheutet

    (University Lyon, INSA Lyon, University Jean Monnet Saint-Etienne, University Claude Bernard Lyon 1, University Lumière·Lyon 2, DISP UR4570, 69621 Villeurbanne, France
    These authors contributed equally to this work.)

Abstract

The concept of Digital Twin (DT) seems promising to improve the management of patient pathways in Emergency Departments (EDs). This article proposes an agent-based architecture of a DT designed for that purpose. The core of this DT is its Information System (IS), which is regularly synchronised on the IS of the Physical Twin (PT). The agents model the ED’s resources (equipment and staff) and patients in the DT and update this information in the DT’s IS. This article shows how such a DT may operate in three modes: (0) “Digital Shadow” to monitor the ED’s current state in real time, (1) “Synchronised DT” to monitor the ED’s current and future states according to a predictive simulation, and (2) “Exploratory DT” in order to perform Monte Carlo simulations of various future states. Mode (1) is the main contribution. This proposition is illustrated in a simulation of the ED in order to demonstrate the capabilities.

Suggested Citation

  • Thierry Moyaux & Yinling Liu & Guillaume Bouleux & Vincent Cheutet, 2023. "An Agent-Based Architecture of the Digital Twin for an Emergency Department," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3412-:d:1066818
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    References listed on IDEAS

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    1. Kaushal, Arjun & Zhao, Yuancheng & Peng, Qingjin & Strome, Trevor & Weldon, Erin & Zhang, Michael & Chochinov, Alecs, 2015. "Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department," Socio-Economic Planning Sciences, Elsevier, vol. 50(C), pages 18-31.
    2. Kung-Jeng Wang & Ying-Hao Lee & Septianda Angelica, 2021. "Digital twin design for real-time monitoring – a case study of die cutting machine," International Journal of Production Research, Taylor & Francis Journals, vol. 59(21), pages 6471-6485, November.
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    Cited by:

    1. Ali Fırat İnal & Çağrı Sel & Adnan Aktepe & Ahmet Kürşad Türker & Süleyman Ersöz, 2023. "A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem," Sustainability, MDPI, vol. 15(10), pages 1-24, May.

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