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Reagents and swab tests during the COVID-19 Pandemic: An optimized supply chain management with UAVs

Author

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  • Colajanni, Gabriella
  • Daniele, Patrizia
  • Sciacca, Daniele

Abstract

In this paper, we develop a supply chain optimization model for the preparation, provision, transportation, and execution of swab tests during COVID-19 pandemic. The proposed approach is based on a multi-tiered network consisting of manufacturing companies of reagents, processing laboratories (where the swab kits are prepared and some swab tests are analyzed), landing stations for UAVs and test centers. As innovations in the supply chain, the sharing of reagents between processing laboratories and the use of UAVs, using 5G technology, are contemplated in the management of the COVID-19 Pandemic. To obtain the optimal solutions of the underlying optimization problem, we provide a variational formulation problem for which results of existence and uniqueness will be provided. Finally, some numerical simulations are examined to validate the effectiveness of our approach.

Suggested Citation

  • Colajanni, Gabriella & Daniele, Patrizia & Sciacca, Daniele, 2022. "Reagents and swab tests during the COVID-19 Pandemic: An optimized supply chain management with UAVs," Operations Research Perspectives, Elsevier, vol. 9(C).
    • Handle: RePEc:eee:oprepe:v:9:y:2022:i:c:s2214716022000288
    DOI: 10.1016/j.orp.2022.100257
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    References listed on IDEAS

    as
    1. Nagurney, Anna, 2021. "Optimization of supply chain networks with inclusion of labor: Applications to COVID-19 pandemic disruptions," International Journal of Production Economics, Elsevier, vol. 235(C).
    2. Santini, Alberto, 2021. "Optimising the assignment of swabs and reagent for PCR testing during a viral epidemic," Omega, Elsevier, vol. 102(C).
    3. Sawik, Tadeusz, 2022. "Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study," Omega, Elsevier, vol. 109(C).
    4. Caruso, Valeria & Daniele, Patrizia, 2018. "A network model for minimizing the total organ transplant costs," European Journal of Operational Research, Elsevier, vol. 266(2), pages 652-662.
    5. Soheyl Khalilpourazari & Hossein Hashemi Doulabi, 2022. "Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec," Annals of Operations Research, Springer, vol. 312(2), pages 1261-1305, May.
    6. Gabriella Colajanni & Patrizia Daniele & Daniele Sciacca, 2022. "On the Provision of Services With UAVs in Disaster Scenarios: A Two-Stage Stochastic Approach," SN Operations Research Forum, Springer, vol. 3(1), pages 1-30, March.
    7. Patrizia Daniele & Antonino Maugeri & Anna Nagurney, 2017. "Cybersecurity Investments with Nonlinear Budget Constraints: Analysis of the Marginal Expected Utilities," Springer Optimization and Its Applications, in: Nicholas J. Daras & Themistocles M. Rassias (ed.), Operations Research, Engineering, and Cyber Security, pages 117-134, Springer.
    Full references (including those not matched with items on IDEAS)

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