Group boarding for airplanes: benchmarking static policies and optimizing dynamic assignment with deep reinforcement learning
Improving boarding efficiency reduces airplane turnaround time and improves passenger experience. Airlines typically assign passengers to a few sequential boarding groups using static seat-based rules. Yet arrivals, seat choices, and luggage are sequential and random, and a static rule ignores the seats earlier passengers have already taken. We propose the first dynamic formulation of boarding group assignment. As each passenger checks in, we observe earlier passengers' seats and groups, the current passenger's seat, and optional luggage information, then assign a group while keeping companions together. We formulate dynamic group assignment as a Markov decision process and solve it with reinforcement learning (RL). The policy uses a convolutional neural network to encode the checked-in seat-assignment state and is trained by proximal policy optimization. The reward balances total boarding time and average individual boarding time. We benchmark the proposed RL policy against three companion-compatible static policies (back-to-front, modified Steffen, and alternating block) in an in-house simulator covering six single- and double-aisle layouts. Back-to-front with optimized group sizes achieves the shortest total boarding time and average individual boarding time among the static benchmarks across all layouts. The dynamic RL policy further outperforms it on both metrics in every layout. On a representative case, the RL policy outperforms the optimal back-to-front by up to 9.8\% in total boarding time and 22.8\% in average individual time. Sweeping the reward weight yields an approximate Pareto frontier for operator choice. Trained policies remain robust under out-of-distribution operating conditions, including varying load factors, companion sizes, and luggage loads.