A multi-product, multi-period vaccine supply chain network planning with injection centers and storage conditions - Reyhane Heydarpour; Hadi Mokhtari; Saeed Dehnavi (PLoS ONE, 2026)
This study develops a mixed-integer linear programming model for a multi-product, multi-period vaccine supply chain network that incorporates injection centers and heterogeneous storage conditions, including cold and ultra-cold refrigeration. The model captures key operational decisions such as distribution center location, vaccine allocation, and storage configuration under capacity and demand constraints, while ensuring no shortage at injection centers. To efficiently solve large-scale instances, a tailored genetic algorithm (GA) is proposed. The main achievements of this research are as follows. First, the proposed model provides an integrated framework that simultaneously considers multi-vaccine characteristics and storage requirements within a three-tier supply chain. Second, computational experiments demonstrate that the GA achieves high-quality solutions with very small optimality gaps compared to exact solutions obtained by GAMS for small and medium-sized problems. Third, the results show that while exact methods become computationally inefficient or infeasible for large-scale instances, the GA remains robust and capable of producing near-optimal solutions within reasonable computational time. Finally, sensitivity analysis confirms the consistency and validity of the model, showing that total cost increases logically with demand and cost parameters. These findings highlight the effectiveness and scalability of the proposed approach and demonstrate its applicability as a decision-support tool for policymakers and healthcare planners in managing complex vaccine distribution systems, particularly during large-scale public health emergencies.