The role of drone technology and application of IoT on vaccine supply chain during a pandemic under uncertain Environment: A real case study of COVID-19 in Iran

Vaccination is a crucial way to combat the pandemic; in other words, vaccines play an important role in controlling the spread of the virus and ultimately ending the pandemic. This study presents a multi-objective mixed integer linear programming model for the vaccine supply chain considering uncertain cost, vaccine purchase, and lead time. Through the utilization of Internet of Things technology, data about various groups is collected. Upon identification of individuals with good health, the specific needs of each area are ascertained during each period. Subsequently, a mathematical model for the vaccine supply chain is presented, encompassing four distinct levels; manufacturers, distribution centers, health centers, and immunization centers. Furthermore, this model incorporates the utilization of drones to deliver vaccines from distribution centers to health centers because of the significant distance between these two levels. The proposed framework encompasses two main goals; minimizing the total cost and the waiting time for people in the queue. A novel fuzzy approach has been employed to deal with the uncertain parameters. The model’s validation is accomplished through the implementation of a real case study of COVID-19 in Iran. The findings indicate that the lack of Internet of Things technology implementation results in a higher number of individuals being directed to immunization centers, thereby elevating the likelihood of infection, and, this scenario leads to the unnecessary administration of vaccines, leading to resource wastage. Additionally, without using drones, vaccines cannot be delivered and injected into people on time. Ultimately, the proposed framework and methodology can be applied in almost larger dimensions and the results demonstrate the model and methods’ efficiency and effectiveness. Since this study is applied to the case study of COVID-19, the findings can be applied in the conditions of similar pandemics.