Multi-objective On-orbit Servicing Mission Planning Based on Greedy Strategy Genetic Algorithm

A mixed optimal control（HOC）model is established to address multi-target spacecraft on-orbit servicing mission planning challenges，incorporating fuel refueling and debris removal scenarios. Discrete state spaces and continuous dynamic systems are formulated within the HOC framework，where Lambert orbital maneuver strategies are employed to derive velocity increments and transfer times. Constraints including spacecraft fuel capacity and mission time windows are integrated，with velocity increments and mission durations defined as optimization objectives through mathematical programming. A greedy strategy genetic algorithm （GS-GA） is proposed to solve the combinatorial optimization problem，combining global exploration capabilities of genetic algorithms with local greedy selection to enhance convergence speed and solution quality. Numerical simulations demonstrate that GS-GA outperforms conventional genetic algorithms in both computational efficiency and solution optimality，providing a validated approach for complex multi-target on-orbit servicing mission planning under dynamic constraints.