  In this paper, we present a decision support framework for optimizing
multiple aspects of vaccine distribution across a multitier 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 being an extension of the
first for the case when multiple vaccines with differing efficacies and costs
are available for the same disease. Vaccine transportation and administration
lead times are also incorporated within the models. We also develop robust
optimization versions of the single vaccine model to account for the impact of
uncertainty in model parameters on the optimal vaccine distribution solution.
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 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 consider the
computational expense of the framework for realistic problem instances, and
suggest multiple preprocessing techniques to reduce their computational burden.
Finally, we also demonstrate how the robust versions of the single vaccine
model outperform the deterministic version under multiple levels of uncertainty
in key model parameters. Our study presents public health authorities and other
stakeholders with a vaccine distribution and capacity planning tool for
multi-tier cold chain networks.
