An Adaptive Memetic Biased Random-Key Genetic Algorithm for Multistage Operational Scheduling of Unmanned Carrier-Based Aircraft Groups

Integrated operational planning for unmanned carrier-based aircraft groups requires the coordination of launch, airborne mission, and recovery activities under complex temporal and resource constraints. Heterogeneous mission workflows and trajectory-dependent arrival times create asymmetric and time-varying demands for limited shared resources. From a resource-utilization symmetry perspective, these demands should be coordinated with available service capacity rather than distributed equally among resources. This study formulates a multistage scheduling model covering launch and departure, formation assembly, aerial refueling, cooperative mission execution, and carrier recovery. A hierarchical objective is adopted to minimize the overall mission cycle first and the total airborne holding time second. To provide realistic temporal inputs, feasible three-dimensional trajectories are generated using the grey wolf optimizer, and their flight times are incorporated into the scheduling model. An adaptive memetic biased random-key genetic algorithm (AMBRKGA) is developed to solve the resulting problem. Its three-layer random-key encoding represents group launch priorities, tanker assignments and receiver service sequences, and recovery priorities. Constraint-based decoding, adaptive diversity control, and variable neighborhood descent are further integrated to generate feasible schedules and coordinate global and local search. Simulations involving 14–24 unmanned carrier-based aircraft produce complete and resource-conflict-free operational plans. In the 24-aircraft scenario, AMBRKGA achieves the shortest mean mission cycle among the comparison algorithms, while ablation and sensitivity analyses indicate effective local refinement and satisfactory parameter robustness. The scope of this work is limited to abstract multistage scheduling and resource coordination and does not address weapon employment, target engagement, or real-time combat command and control. Overall, the proposed method effectively coordinates asymmetric resource demands across operational stages and improves multistage operational efficiency.