This paper proposes a continuous-time mixed-integer linear programming (MILP) that integrates spatial placement and time-continuous scheduling to minimize aircraft maintenance hangar operating costs. It overcomes the scalability limitations of existing approaches by simultaneously optimizing aircraft placement and timing. The proposed model is compared with existing research benchmarks, exploring large-scale performance and quantifying its sensitivity to temporal congestion. It achieves orders of magnitude speedup over literature benchmarks, solving long-standing congested instances in 0.11 seconds and finding proven optimal solutions for instances with up to 40 aircraft. For large-scale problems, it finds solutions with small optimality margins within a one-hour time limit for instances with up to 80 aircraft and provides strong bounds for problems with up to 160 aircraft. The optimized plan consistently increases hangar throughput (e.g., +33% in-service aircraft compared to the heuristic on instance RND-N030-I03), reducing delay penalties and improving asset utilization. These results demonstrate that accurate optimization has become computationally feasible for large-scale hangar planning, providing a validated tool for balancing solution quality and computation time for strategic and operational decision-making.
