Reducing the Prevalence of Coronavirus (COVID-19) in Airlines Based on and the Reinforcement Artificial Intelligence

This paper proposes a method based on the artificial intelligence reinforcement Q-learning algorithm and paired comparison technique to solve the problem of health monitoring devices shortage in airlines. In this research, Kish Airline destinations considered as a case study. By considering the importance of continuing air travels during covid-19 pandemic, one of the most effective ways for decreasing the risk of COVID-19 infections in air travels is establishing the health monitoring stations at the airport gates. In view of the enormous number of airports and airlines routes, nationwide coverage of them by health monitoring stations is unimaginable. Therefore, in this paper, artificial intelligence reinforcement Q-learning algorithm functionality in estimating the risk of infecting the COVID-19 virus was considered for the Kish Airline destinations. Furthermore, the optimal policy for the distribution of the health monitoring devices is designed based on the computational model.