Approximate dynamic programming for vaccine distribution with uncertain supply and limited resources

We study the vaccine distribution problem over a three-echelon network during a sudden pandemic outbreak. We consider uncertainties in vaccine supply due to delays in production, disruption in global supply chain, or lockdown policies in the early phases of the pandemic outbreak. We also consider limited resources including cold trucks for vaccine transportation and healthcare workers for vaccine administration. The problem is to dynamically allocate these limited resources over the distribution network in response to periodic and uncertain vaccine supply, such that vaccine can be transported and administered in a timely and cost-effective manner. We formulate a dynamic programming model for this problem and propose solution algorithms based on approximate dynamic programming. We conduct a case study on a vaccine distribution network in Iran and report computational results that demonstrate the superiority of our solution approach over three benchmark approaches. We also perform various sensitivity analyses and provide useful insights into the benefits of more transportation and human resources, and the impacts of vaccine shelf life and vaccine supply amount.