A Space Telescope Scheduling Approach Combining Observation Priority Coding with Problem Decomposition Strategies.

Area of Science:

• Astronomy and Astrophysics
• Space Surveillance and Tracking
• Operations Research

Background:

• The growing volume of space debris necessitates enhanced capabilities for its observation and tracking.
• Telescope observation scheduling for space debris is a complex problem, particularly at large scales, requiring efficient algorithms.
• Existing methods may struggle with the computational demands of large-scale space debris observation scheduling.

Purpose of the Study:

• To develop a practical heuristic algorithm for optimizing telescope observation schedules for space debris.
• To address the challenge of large-scale problems by improving algorithm solving speed.
• To maximize the observation value within the time constraints of space debris visibility.

Main Methods:

• A large-scale problem is decomposed into smaller, manageable sub-problems based on observation time.
• A novel coding and decoding method, prioritizing targets entering the queue, is developed for each sub-problem.
• Adaptive variable neighborhood search is employed to generate observation plans for individual sub-problems.

Main Results:

• The proposed heuristic algorithm effectively solves the space debris observation scheduling problem.
• Problem partitioning significantly accelerates the solving speed for large-scale instances.
• Combining solutions from sub-problems yields a comprehensive observation plan for the original problem.

Conclusions:

• The developed heuristic algorithm offers a practical and efficient solution for space debris observation scheduling.
• The partitioning strategy and adaptive variable neighborhood search are key to handling large-scale challenges.
• This approach enhances the capability for effective space surveillance and debris management.