Fuzzy optimization model for a blood supply network in the post-disaster phase, considering a branch-and-cut approach

This study employs a fuzzy methodology to investigate the key elements of establishing an effective blood supply chain (BSC) in the post-COVID era. An analysis of the pandemic’s impact on the logistics and management of blood supply indicates that adaptive techniques are necessary to ensure a robust and responsive blood distribution system. This project aims to enhance decision- making processes in blood supply chain management (BSCM) through fuzzy logic, addressing the uncertainties and complexities introduced by the global health crisis. The proposed fuzzy methodology offers a flexible framework for optimizing resource allocation, transportation routes, and inventory levels, thereby improving the effectiveness and efficiency of blood donation following the COVID-19 pandemic. In this work, we developed a mixed-integer linear programming (MILP) model for the BSC that considers the uncertainty of supply and demand from the point of receiving blood from donors to the distribution points at demand centers. The MILP minimizes blood product shortages and expiration rates while reducing costs associated with the BSC. A Markov chain was employed to address the unpredictability of the blood donation supply. The demand is treated as a fuzzy estimate of the medical center market. The proposed model is solved using the branch-and-cut (B&C) algorithm. The results are presented alongside a case study that demonstrates the applicability of the suggested framework, showing a reduction in costs as well as decreased scarcity and timelines for blood-related commodities in the BSC.