A bi-objective mathematical model to respond to COVID-19 pandemic

COVID-19 has infected more than 543 million people and killed more than 6 million people since it was first diagnosed in Wuhan, China, on December 1, 2019. Vaccines were needed to combat the epidemic from the start of the pandemic due to the high incidence of morbidity and mortality. After the development of vaccines, due to the need for extensive vaccination to stop the spread of the disease, the supply chain of COVID-19 vaccines and the need to develop mathematical optimization models became vital. Lack of admission capacity at vaccination centers is one of the main problems facing vaccination, which slows down the process and increases infection risk. For this purpose, this paper proposes a mathematical optimization model for the COVID-19 vaccine supply chain network design, considering two objectives: maximizing the minimum demand coverage and minimizing the total time. With its equitable approach, the first objective function increases demand coverage. A second objective function accelerates vaccination by optimizing activities like allocating vaccines from storage centers to distribution centers and reducing the risk of spreading diseases by reducing transportation times to vaccination centers. According to this model, temporary vaccination centers can enhance or maintain vaccination rates by supplementing existing vaccination centers’ admission capacity. Two numerical examples were used to validate the proposed mathematical model. The model’s performance was then assessed using sensitivity analysis on its key parameters, demonstrating the effectiveness of temporary vaccination centers.