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Remote-Sensing Satellite Mission Scheduling Optimisation Method under Dynamic Mission Priorities

Author

Listed:
  • Xiuhong Li

    (College of Information Science and Engineering (School of Cyber Science and Engineering), Xinjiang University, Urumqi 830046, China)

  • Chongxiang Sun

    (School of Computer Science and Engineering, Central South University, Changsha 410075, China)

  • Huilong Fan

    (School of Computer Science and Engineering, Central South University, Changsha 410075, China)

  • Jiale Yang

    (College of Information Science and Engineering (School of Cyber Science and Engineering), Xinjiang University, Urumqi 830046, China)

Abstract

Mission scheduling is an essential function of the management control of remote-sensing satellite application systems. With the continuous development of remote-sensing satellite applications, mission scheduling faces significant challenges. Existing work has many inherent shortcomings in dealing with dynamic task scheduling for remote-sensing satellites. In high-load and complex remote sensing task scenarios, there is low scheduling efficiency and a waste of resources. The paper proposes a scheduling method for remote-sensing satellite applications based on dynamic task prioritization. This paper combines the and Bound methodologies with an onboard task queue scheduling band in an active task prioritization context. A purpose-built emotional task priority-based scheduling blueprint is implemented to mitigate the flux and unpredictability characteristics inherent in the traditional satellite scheduling paradigm, improve scheduling efficiency, and fine-tune satellite resource allocation. Therefore, the Branch and Bound method in remote-sensing satellite task scheduling will significantly save space and improve efficiency. The experimental results show that comparing the technique to the three heuristic algorithms (GA, PSO, DE), the BnB method usually performs better in terms of the maximum value of the objective function, always finds a better solution, and reduces about 80% in terms of running time.

Suggested Citation

  • Xiuhong Li & Chongxiang Sun & Huilong Fan & Jiale Yang, 2024. "Remote-Sensing Satellite Mission Scheduling Optimisation Method under Dynamic Mission Priorities," Mathematics, MDPI, vol. 12(11), pages 1-28, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:11:p:1704-:d:1405593
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    References listed on IDEAS

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    1. Kemmoé Tchomté, Sylverin & Gourgand, Michel, 2009. "Particle swarm optimization: A study of particle displacement for solving continuous and combinatorial optimization problems," International Journal of Production Economics, Elsevier, vol. 121(1), pages 57-67, September.
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