A decision support framework for optimal vaccine distribution across a multi-tier cold chain network

Abstract

In this paper, we present a decision support framework for optimizing multiple aspects of vaccine distribution across a multi-tier cold chain network. We propose two multi-period optimization formulations within this framework: first to minimize inventory, ordering, transportation, personnel and shortage costs associated with a single vaccine; the second for the case when multiple vaccines with differing efficacies and costs are available for the same disease. We use the case of the Indian state of Bihar and COVID-19 vaccines to illustrate the implementation of the framework. We present computational experiments including what-if scenario and sensitivity analyses to demonstrate: (a) the organization of the model outputs; (b) how the models can be used to assess the impact of cold chain point storage capacities, transportation vehicle capacities, and manufacturer capacities on the optimal vaccine distribution pattern; and (c) the impact of vaccine efficacies and associated costs such as ordering and transportation costs on the vaccine selection decision informed by the model. We then demonstrate how robust optimization versions of the single vaccine model, with box and budgeted uncertainty sets, outperform the deterministic version under multiple levels of uncertainty in key model parameters. Finally, we also consider the computational expense of the framework for realistic problem instances, and suggest multiple preprocessing techniques to reduce computational runtimes. Our study presents public health authorities and other stakeholders with a vaccine distribution and capacity planning tool for multi-tier cold chain networks.