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A network model for minimizing the total organ transplant costs

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  • Caruso, Valeria
  • Daniele, Patrizia

Abstract

Nowadays, many organs such as kidney, liver, pancreas, intestine, heart, as well as lungs, can be safely transplanted. Sometimes organ transplantation is the only possible therapy, for instance for patients with end-stage liver diseases, and the preferred treatment, for instance for patients with end-stage renal diseases. As a consequence, the demand of organs has greatly exceeded the offer and has become a key tool to cure diseases. In many countries the costs to receive an organ, which are often very expensive, are all charged by the National Health Service. In our paper, we aim at presenting a mathematical model, based on networks, which allows us to minimize the total costs associated with organ transplants. We find the related optimality conditions and the variational inequality formulation. Some existence and uniqueness results as well as the Lagrange formulation are stated and some numerical examples are studied.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:266:y:2018:i:2:p:652-662
    DOI: 10.1016/j.ejor.2017.09.040
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    References listed on IDEAS

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    5. Nagurney, Anna & Shukla, Shivani, 2017. "Multifirm models of cybersecurity investment competition vs. cooperation and network vulnerability," European Journal of Operational Research, Elsevier, vol. 260(2), pages 588-600.
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    Cited by:

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    3. Mendonça, Francisco V. & Catalão-Lopes, Margarida & Marinho, Rui Tato & Figueira, José Rui, 2020. "Improving medical decision-making with a management science game theory approach to liver transplantation," Omega, Elsevier, vol. 94(C).
    4. 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.
    5. 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).
    6. Alireza Goli & Ali Ala & Seyedali Mirjalili, 2023. "A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty," Annals of Operations Research, Springer, vol. 328(1), pages 493-530, September.
    7. Anna Nagurney, 2022. "Supply chain networks, wages, and labor productivity: insights from Lagrange. analysis and computations," Journal of Global Optimization, Springer, vol. 83(3), pages 615-638, July.
    8. Gabriella Colajanni & Patrizia Daniele, 2022. "A Mathematical Network Model and a Solution Algorithm for IaaS Cloud Computing," Networks and Spatial Economics, Springer, vol. 22(2), pages 267-287, June.

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