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Two-Stage Satellite Combined-Task Scheduling Based on Task Merging Mechanism

Author

Listed:
  • Jing Yu

    (School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410004, China)

  • Jiawei Guo

    (School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410004, China)

  • Lining Xing

    (Key Laboratory of Collaborative Intelligence Systems, Xidian University, Xi’an 710126, China
    Hunan Quanyong Information Technology Co., Ltd., Changsha 410100, China)

  • Yanjie Song

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Zhaohui Liu

    (School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410004, China)

Abstract

Satellites adopt a single-task observation mode in traditional patterns. Although this mode boasts high imaging accuracy, it is accompanied by a limited number of observed tasks and a low utilization rate of satellite resources. This limitation becomes particularly pronounced when dealing with extensive and densely populated observation task sets because the inherent mobility of satellites often leads to conflicts among numerous tasks. To address this issue, this paper introduces a novel multi-task merging mechanism aimed at enhancing the observation rate of satellites by resolving task conflicts. Initially, this paper presents a task merging method based on the proposed multitask merging mechanism, referred to as the constrained graph (CG) task merging approach. This method can merge tasks while adhering to the minimal requirements specified by users. Subsequently, a multi-satellite merging scheduling model is established based on the combined task set. Considering the satellite combined-task scheduling problem (SCTSP), an enhanced fireworks algorithm (EFWA) is proposed that incorporates the CG task synthesis mechanism. This algorithm incorporates local search strategies and a population disturbance mechanism to enhance both the solution quality and convergence speed. Finally, the efficacy of the CG algorithm was validated through a multitude of simulation experiments. Moreover, the effectiveness of the EFWA is confirmed via extensive comparisons with other algorithms, including the basic ant colony optimization (ACO) algorithm, enhanced ant colony optimization (EACO) algorithm, fireworks algorithm (FWA), and EFWA. When the number of tasks in the observation area are dense, such as in the case where the number of tasks is 700, the CG + EFWA (CG is adopted in the task merging stage and EFWA is adopted in the satellite combined-task scheduling stage) method improves observation benefits by 70.35% (compared to CG + EACO, CG is adopted in the task merging stage and EACO is adopted in the satellite combined-task scheduling stage), 78.93% (compared to MS + EFWA, MS is adopted in the task merging stage and EFWA is adopted in the satellite combined-task scheduling stage), and 39.03% (compared to MS + EACO, MS is adopted in the task merging stage and EACO is adopted in the satellite combined-task scheduling stage).

Suggested Citation

  • Jing Yu & Jiawei Guo & Lining Xing & Yanjie Song & Zhaohui Liu, 2024. "Two-Stage Satellite Combined-Task Scheduling Based on Task Merging Mechanism," Mathematics, MDPI, vol. 12(19), pages 1-22, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:3107-:d:1492101
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    References listed on IDEAS

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