Abstract:

Flight test mission planning is critical to aircraft performance evaluation, but traditional manual methods often struggle with extensive solution spaces and complex constraints. This paper constructs an overall flight test mission planning model and introduces a flight test planning algorithm (FTPA) based on adaptive large neighborhood search. FTPA utilizes Kahn topological sorting to generate an initial test-item priority sequence, which is then decoded into an initial solution, and jointly schedules flight test items and test aircraft within the adaptive large neighborhood search framework. A comparison with the CPLEX solver shows that the FTPA algorithm yields solutions with a gap of less than 0.2% from the theoretical optimum within seconds, demonstrating superior solution quality and efficiency in obtaining optimal or high-quality feasible solutions. Sensitivity analyses of key factors in flight tests further validate the robustness and stability of the algorithm. The proposed approach provides support for optimizing flight test mission management.

Key words: flight test, mission planning, mixed integer programming, adaptive large neighborhood search, sensitivity analysis