A Bi-objective Organ Transplant Supply Chain Network with Recipient Priority Considering Carbon Emission Under Uncertainty, a Case Study

As one of the most complicated and challenging networks among healthcare systems, the organ transplant network necessitates an effective supply chain network design. In this article, a bi-objective mixed integer nonlinear programming (MINLP) location-allocation model is proposed to design the organ transplant supply chain network, with the objectives of minimizing overall costs (including strategical and operational costs) and the number of unsatisfied demands under uncertainty. The developed model calculates the optimum number of facilities to be established and equipped for each organ, the flows between them, and the optimal allocation of cold chain vehicles, which is a combination of similar works in this context with cold chain and resource allocation as one of the novelties of this paper. Moreover, the preciousness of human life necessitates a policy for allocating organs. Hence, in this study, high-risk recipients, who are more likely to die in case of unmet demand, are prioritized above low-risk ones to prevent mortality as much as possible. This article also takes transportation constraints into account in the effort to minimize carbon emissions, one of the most challenging environmental concerns of the present day. Numerical experiments demonstrate the applicability of the developed model, and a case study is presented to compute the optimal solutions of the proposed methodology. Finally, various sensitivity analyses are performed to provide managerial insights.