Group boarding for airplanes: benchmarking static policies and optimizing dynamic assignment with deep reinforcement learning
创建于 更新于
摘要
This paper introduces a dynamic boarding group assignment framework for commercial aircraft, formulating the problem as a finite-horizon MDP and solving it with proximal policy optimization (PPO) using a CNN-based state encoder. The model integrates seat location, carry-on luggage declarations, and travel-companion constraints. In simulations across six layouts, the dynamic policy consistently outperforms strong static benchmarks (back-to-front with optimized group sizes) on both total boarding time and average individual boarding time, achieving up to 9.8% and 22.8% improvements respectively. The policy generalizes to varying load factors, companion distributions, and luggage loads, and a Pareto frontier is reported by sweeping the objective weight. These results suggest online dynamic grouping at check-in can meaningfully reduce turnaround time while improving passenger experience [page::6][page::7].
