A Bilevel Programming Approach for Optimizing Multi-Satellite Collaborative Mission Planning

Area of Science:

• Space Technology
• Remote Sensing
• Operations Research

Background:

• Multi-satellite collaborative mission planning is crucial for efficient Earth observation.
• Increasing mission complexity and volume necessitate advanced planning strategies.
• Current planning often separates mission assignment and resource scheduling, leading to suboptimal outcomes.

Purpose of the Study:

• To develop a joint decision-making framework for multi-satellite collaborative mission planning.
• To optimize mission completion rate, profit rate, and satellite resource utilization.
• To address the intricate two-stage decision process of mission assignment and resource scheduling.

Main Methods:

• Formulation of the problem as a bilevel programming model from a game-theoretic perspective.
• Proposal of a nested bilevel improved genetic algorithm (NBIGA) for solving the model.
• Simulation experiments to validate the model and algorithm's performance.

Main Results:

• The bilevel programming model effectively integrates mission assignment and resource scheduling.
• The NBIGA demonstrates robustness and superior optimization capabilities.
• Simulation results confirm improved mission completion, profit rates, and resource utilization.

Conclusions:

• Joint decision-making in multi-satellite mission planning is feasible and beneficial.
• The proposed NBIGA offers enhanced optimization outcomes and efficiency compared to traditional methods.
• This research advances the field of intelligent satellite mission planning and Earth observation.