The efficient scheduling of airline core resources is crucial for enhancing flight operation efficiency and reducing operational costs. This paper proposes a novel aircraft maintenance routing planning model that incorporates uncertain maintenance time for two types of core resources： Flights and aircraft. The model simultaneously considers key factors such as flight delay propagation， dynamic remaining capacity of maintenance stations， and uncertainty in aircraft maintenance duration. To solve this complex model， the Dantzig-Wolfe decomposition method is introduced to break it down into a master problem and pricing sub-problems. An improved branch-and-price algorithm， which integrates a labeling algorithm to solve the sub-problems， is designed. Numerical experiments verify the effectiveness of the proposed model and the algorithm. The results demonstrate that， compared to traditional models， the proposed model with uncertain maintenance access time can effectively alleviate congestion at maintenance stations， reducing the maximum simultaneous maintenance queue by up to 50%. As the fleet size increases， the delay time can be reduced by up to 87.5%. Compared to IBM’s CPLEX， the proposed algorithm achieves a maximum reduction in solution time of 67.1% for small and medium-scale instances， and a 14% reduction for large-scale instances while obtaining the same optimal solution.