Vaccine Distribution and Inoculation Chain Design in Pandemic Conditions (Case Study of Corona Vaccine)

This research has presented a three-objective nonlinear mathematical programming model for optimizing the vaccine distribution chain in the pharmaceutical industry. With regard to stability issues, this involves considering location allocation, inventory under uncertain demand, and a crowded queue system with regard to stability and minimizing vaccination waiting times, ordering costs, vaccine holding costs, establishing new vaccination centers, and reducing harmful environmental waste caused by vaccinations and establishing new vaccination centers. As this problem is NP-hard, the NSGA_II algorithm was used. In a numerical example, sensitivity analysis showed that increasing the average waiting time decreased the total costs of the problem, while increasing the amount of generated garbage increased the total costs of the network. Moreover, a sensitivity analysis of the model at various levels of uncertainty revealed that with an increase in uncertainty, the network will have more people who need vaccines, which increases the network's costs. Due to the limited capacity of the centers and the fixed parameters of the network, costs increase. In contrast, the increased number of vaccines ordered and ready for distribution results in an increase in the average waiting time in distribution centers and waste. As a result of reducing the capacity of inoculation centers due to the fixed amount of vaccine orders, more centers were built, resulting in an increase in construction costs and increase in greenhouse gases due to the construction of a new center and transportation. Thus, the rate of entry to each center will decrease, as well as the length of the queue, ultimately leading to a decrease in average waiting time. In addition, 15 numerical examples have been examined to demonstrate the efficiency and design of the proposed algorithm.