The corona pandemic significantly changes the processes of aircraft and passenger handling at the airport. In our contribution, we focus on the time-critical process of aircraft boarding, where regulations regarding physical distances between passengers will significantly increase
boarding time. The passenger behaviour is implemented in a field-validated stochastic cellular automata model, which is extended by a module to evaluate the transmission risk. We propose an improved boarding process by considering that most of the passengers travel together and should be boarded and seated as a group. The NP-hard seat allocation of groups with minimized individual interactions between groups is solved with a genetic algorithm. Then, the improved seat allocation is used to derive an associated boarding sequence aiming at both short boarding times and a low risk of virus transmission. Our results show that the consideration of
groups will significantly contribute to a faster boarding (reduction of time by about 60%) and less transmission risk (reduced by 85%) compared to the standard random boarding procedures applied in the pandemic scenario.