Team 2056 Page 2 of 50 We validated our model using tests for rigor in both robustness and sen sitivity. We find that in robustness test cases that our model makes pre- dictions that correlate well with empirical evidence We simulate many different a priori configurations, such as back to front window to aisle and alternate half-rows When normalized to a random boarding sequence, we found that window to aisle, the best performing pattern, improved efficiency by 36% on average. Even more surprising he most common technique, zone boarding, performed even worse than random. We compare these techniques to novel boarding sequences de- veloped using our genetic algorithm Based on the output of our genetic algorithm, we recommend a hybrid boarding process: a combination of window to aisle and alternate half- ows. This technique is a three-zone process, like window to aisle, but it allows family units to board first, simultaneously with window seatTeam 2056 Page 2 of 50 We validated our model using tests for rigor in both robustness and sen￾sitivity. We find that in robustness test cases that our model makes pre￾dictions that correlate well with empirical evidence. We simulate many different a priori configurations, such as back to front, window to aisle and alternate half-rows. When normalized to a random boarding sequence, we found that window to aisle, the best performing pattern, improved efficiency by 36% on average. Even more surprising, the most common technique, zone boarding, performed even worse than random. We compare these techniques to novel boarding sequences de￾veloped using our genetic algorithm. Based on the output of our genetic algorithm, we recommend a hybrid boarding process; a combination of window to aisle and alternate half￾rows. This technique is a three-zone process, like window to aisle, but it allows family units to board first, simultaneously with window seat passengers