  This paper proposed a novel large neighborhood search-adaptive genetic
algorithm (LNS-AGA) for many-to-many on-orbit repairing mission planning of
geosynchronous orbit (GEO) satellites with mission deadline constraint. In the
many-to-many on-orbit repairing scenario, several servicing spacecrafts and
target satellites are located in GEO orbits which have different inclination,
RAAN and true anomaly. Each servicing spacecraft need to rendezvous with target
satellites to perform repairing missions under limited fuel. The mission
objective is to find the optimal servicing sequence and orbit rendezvous time
of every servicing spacecraft to minimize total cost of all servicing
spacecrafts with all target satellites repaired. Firstly, a time-dependent
orbital rendezvous strategy is proposed, which can handle the mission deadline
constraint. Besides, it is also cost-effective compared with the existing
strategy. Based on this strategy, the many-to-many on-orbit repairing mission
planning model can be simplified to an integer programming problem, which is
established based on the vehicle routing problem with time windows (VRPTW)
model. In order to efficiently find a feasible optimal solution under
complicated constraints, a hybrid adaptive genetic algorithm combining the
large neighborhood search procedure is designed. The operations of "destroy"
and "repair" are used on the elite individuals in each generation of the
genetic algorithm to enhance local search capabilities. Finally, the
simulations under different scenarios are carried out to verify the
effectiveness of the presented algorithm and orbital rendezvous strategy, which
performs better than the traditional genetic algorithm.
