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A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory

Author

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  • Karl-Arne Johannessen

    (The Intervention Center, Oslo University Hospital, 0424 Oslo, Norway
    Faculty of Medicine, University of Oslo, 0318 Oslo, Norway)

  • Hans Comtet

    (The Intervention Center, Oslo University Hospital, 0424 Oslo, Norway
    The Department of Design, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

  • Erik Fosse

    (The Intervention Center, Oslo University Hospital, 0424 Oslo, Norway
    Faculty of Medicine, University of Oslo, 0318 Oslo, Norway)

Abstract

We present a model for drone transport of the complete annual analytic volume of 6.5 million analyses—(routine and emergency) between two inner-city university laboratories at Oslo University Hospital located 1.8 km apart and with a time restriction for the analyses of no more than 60 min. The total laboratory activity was analyzed per min for the complete year of 2018. The time from the clinical ordering of tests to the loading of the drone, drone transport time, and analysis time after the sample arrived at the analyzing laboratory were assessed using the lead time of emergency analyses of C-reactive protein, troponin, and the international normalized ratio. The activity had characteristic diurnal patterns, with the most intensive traffic between 8 and 12 a.m. on weekdays and there being considerably less traffic for the rest of the day, at night and on weekends. Drone schedules with departures 15–60 min apart were simulated. A maximum of 15 min between flights was required to meet the emergency demand for the analyses being completed within 60 min. The required drone weight capacity was below 3.5 kg at all times. In multiple simulations, the drone times were appropriate, whereas variations in the clinic- and laboratory-related time intervals caused violations of the allowed time 50% of the time. Drone transport with regular schedules may potentially improve the transport time compared with traditional ground transport and allow the merging of large laboratories, even when the demand for emergency analyses restricts the maximum transport time. Comprehensive economic evaluations and robust drone technology are needed before such solutions can be ready for implementation.

Suggested Citation

  • Karl-Arne Johannessen & Hans Comtet & Erik Fosse, 2021. "A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory," IJERPH, MDPI, vol. 18(9), pages 1-19, April.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:9:p:4580-:d:543951
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    References listed on IDEAS

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    1. Shreeranga Bhat & E.V. Gijo & N.A. Jnanesh, 2014. "Application of Lean Six Sigma methodology in the registration process of a hospital," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 63(5), pages 613-643, June.
    2. Zoe Radnor & Paul Walley, 2008. "Learning to Walk Before We Try to Run: Adapting Lean for the Public Sector," Public Money & Management, Taylor & Francis Journals, vol. 28(1), pages 13-20, February.
    3. Andrea Chiarini & Enrico Bracci, 2013. "Implementing Lean Six Sigma in healthcare: issues from Italy," Public Money & Management, Taylor & Francis Journals, vol. 33(5), pages 361-368, September.
    4. David Pisinger & Stefan Ropke, 2019. "Large Neighborhood Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 99-127, Springer.
    5. Jiyoon Park & Solhee Kim & Kyo Suh, 2018. "A Comparative Analysis of the Environmental Benefits of Drone-Based Delivery Services in Urban and Rural Areas," Sustainability, MDPI, vol. 10(3), pages 1-15, March.
    6. Taş, Duygu & Gendreau, Michel & Jabali, Ola & Laporte, Gilbert, 2016. "The traveling salesman problem with time-dependent service times," European Journal of Operational Research, Elsevier, vol. 248(2), pages 372-383.
    7. Chiang, Wen-Chyuan & Li, Yuyu & Shang, Jennifer & Urban, Timothy L., 2019. "Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization," Applied Energy, Elsevier, vol. 242(C), pages 1164-1175.
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