An Efficient Continuous-Time MILP for Integrated Aircraft Hangar Scheduling and Layout

TL;DR

This paper introduces a scalable continuous-time MILP model for aircraft hangar scheduling and layout, significantly improving solution speed and quality over previous methods, enabling practical large-scale operational planning.

Contribution

It presents a novel continuous-time MILP formulation that efficiently integrates aircraft placement and scheduling, overcoming previous scalability limitations and enabling large-scale optimization.

Findings

Achieves orders-of-magnitude speedups over prior models

Solves large congested instances in 0.11 seconds

Provides high-quality solutions for up to 80 aircraft within one hour

Abstract

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.