This study proposes a four-stage blood supply chain network for crisis conditions, integrating donor groups, permanent/temporary blood collection centers, regional blood centers, and hospitals. A multi-objective, multi-period integer linear programming model optimizes blood distribution using unmanned aerial vehicles (UAVs), ambulances, and vehicles to minimize total supply chain costs and maximum travel times. Computational experiments and a real-world case study in Kurdistan province, Iran, demonstrate that UAVs with higher speeds (150 km/h) reduce travel times by up to 35% and costs by 22% compared to baseline (100 km/h), while increasing UAV capacity from 1.6 kg to 2.2 kg decreases Pareto optimal solutions by 16%, indicating improved efficiency. Deploying 150 UAVs (vs. 110) shifts the Pareto front, lowering costs by 18% and maximum travel times by 1.2 hours. Sensitivity analyses reveal UAV specifications critically impact performance, with optimal blood allocation achieved when donor groups supply centralized processing. The epsilon-constraint method solves problems of varying scales, with CPLEX achieving solutions for medium instances in under 40 minutes, highlighting UAVs’ role in enhancing crisis-response blood supply chains.
