A multi-objective integrated supply chain model under dynamic allocation of temporary vaccine centers and uncertain demand

The vaccine supply chain is a complex, multi-tiered system that ensures the efficient production, storage, distribution, and delivery of vaccines to healthcare facilities and populations worldwide. To address research gaps identified in the literature, this study proposes a new multi-objective integrated supply chain model for vaccine distribution. Our contribution lies in designing a model that accounts for multiple periods and supply chain echelons, allowing vaccine centers to be relocated or reopened based on demand fluctuations. Additionally, resources within these centers (vaccine stations) are optimally allocated to enhance efficiency. Given the uncertainty in vaccine demand, we formulate a robust counterpart model using a budget uncertainty set, incorporating predefined bounds to improve solution quality. The model includes three objective functions: minimizing rectilinear distance between supply chain echelons, reducing total costs across the distribution network, and minimizing environmental impact. These objectives are formulated using the goal programming approach to achieve a balanced solution. Due to the complexity of large-scale problems, we employ the K-Means clustering method to enhance model tractability while maintaining solution quality. The model's efficiency is validated through a case study, showing that as the number of clusters increases from 5 to 35, the total supply chain cost rises by 2.8 %, while the environmental impact decreases by 3.1 %. These results validate trade-offs between the number of clusters and overall system performance. Fewer clusters lead to longer distances between demand zones, increasing transportation costs and emissions, while more clusters result in higher logistics costs due to increased travel and facility use.