An Efficient Continuous-Time MILP for Integrated Aircraft Hangar Scheduling and Layout
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| Format: | Preprint |
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2025
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| author | Pazhooh, Shayan Farhang Shemirani, Hossein Shams |
| author_facet | Pazhooh, Shayan Farhang Shemirani, Hossein Shams |
| contents | Efficient management of aircraft MRO hangars requires the integration of spatial layout with time-continuous scheduling to minimize operational costs. We propose a continuous-time mixed-integer linear program that jointly optimizes aircraft placement and timing, overcoming the scalability limits of prior formulations. A comprehensive study benchmarks the model against a constructive heuristic, probes large-scale performance, and quantifies its sensitivity to temporal congestion. The model achieves orders-of-magnitude speedups on benchmarks from the literature, solving a long-standing congested instance in 0.11 seconds, and finds proven optimal solutions for instances with up to 40 aircraft. Within a one-hour limit for large-scale problems, the model finds solutions with small optimality gaps for instances up to 80 aircraft and provides strong bounds for problems with up to 160 aircraft. Optimized plans consistently increase hangar throughput (e.g., +33% serviced aircraft vs. a heuristic on instance RND-N030-I03), leading to lower delay penalties and higher asset utilization. These findings establish that exact optimization has become computationally viable for large-scale hangar planning, providing a validated tool that balances solution quality and computation time for strategic and operational decisions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_02640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Efficient Continuous-Time MILP for Integrated Aircraft Hangar Scheduling and Layout Pazhooh, Shayan Farhang Shemirani, Hossein Shams Optimization and Control Artificial Intelligence Computational Engineering, Finance, and Science 90C11 (Primary), 90B35, 90C27 (Secondary) Efficient management of aircraft MRO hangars requires the integration of spatial layout with time-continuous scheduling to minimize operational costs. We propose a continuous-time mixed-integer linear program that jointly optimizes aircraft placement and timing, overcoming the scalability limits of prior formulations. A comprehensive study benchmarks the model against a constructive heuristic, probes large-scale performance, and quantifies its sensitivity to temporal congestion. The model achieves orders-of-magnitude speedups on benchmarks from the literature, solving a long-standing congested instance in 0.11 seconds, and finds proven optimal solutions for instances with up to 40 aircraft. Within a one-hour limit for large-scale problems, the model finds solutions with small optimality gaps for instances up to 80 aircraft and provides strong bounds for problems with up to 160 aircraft. Optimized plans consistently increase hangar throughput (e.g., +33% serviced aircraft vs. a heuristic on instance RND-N030-I03), leading to lower delay penalties and higher asset utilization. These findings establish that exact optimization has become computationally viable for large-scale hangar planning, providing a validated tool that balances solution quality and computation time for strategic and operational decisions. |
| title | An Efficient Continuous-Time MILP for Integrated Aircraft Hangar Scheduling and Layout |
| topic | Optimization and Control Artificial Intelligence Computational Engineering, Finance, and Science 90C11 (Primary), 90B35, 90C27 (Secondary) |
| url | https://arxiv.org/abs/2508.02640 |