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Acta Aeronautica et Astronautica Sinica

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Multi-objective Scheduling Optimization Method for Relay Satellites Considering User Preferences

  

  • Received:2024-08-19 Revised:2024-11-22 Online:2024-11-26 Published:2024-11-26
  • Contact: Qian YIN
  • Supported by:
    Independent Innovation Science Foundation Project of National University of Defense Technology

Abstract: As China's space station continues its long-term operations and scientific experiments, the demand for relay satellites has significant-ly increased, characterized by high frequency, multiple tasks, and diverse services. This complex demand urgently requires more flexible and efficient scheduling solutions for relay satellites to meet the personalized service needs of users. Therefore, this paper proposes an innovative application model for relay satellites, focusing on user preferences and allowing users to submit multiple optional service time windows, as well as specifying the desired execution antennas for each task. To address this new model, we construct a comprehensive scheduling model for relay satellites that considers task completion rates, user satisfaction, antenna load balancing, and task priority. We also design a multi-objective scheduling algorithm based on a voting mechanism. This algorithm integrates various multi-objective scheduling methods and adaptively adjusts the weights of these methods during the optimization process, ensuring the selection of the optimal scheduling strategy at different stages. To validate the effectiveness of the proposed model and algorithm, extensive simulation experiments were conducted. The results demonstrate that our method has significant advantages in solving multi-objective scheduling problems for relay satellites, showing remarkable improvements in user satisfaction and system service capacity compared to multi-objective algorithms such as NSGA-II, NSGA-III, BiGE, GrEA, MOEA/D, and AMODSA.

Key words: Relay Satellites, User Preferences, Dynamic Scheduling, Multi-objective Optimization, self-adaptive

CLC Number: