面向云层覆盖不确定性下多颗敏捷卫星调度的基于模拟退火的启发式算法
Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty
AI总结：
针对云层覆盖不确定性下多颗敏捷地球观测卫星的调度收益最大化问题，首次结合机会约束规划与样本近似处理不确定性，提出融合快速插入策略的改进模拟退火启发式算法，经实验验证其效率与效果均优于其他算法。
摘要：
敏捷卫星是新一代地球观测卫星（EOSs），具备更强的姿态机动能力。由于星上搭载的光学遥感仪器无法穿透云层，云层覆盖会对卫星观测任务产生显著影响。我们首次研究了云层覆盖不确定性下的多颗敏捷EOSs调度问题，其目标为最大化总观测收益。我们首先采用机会约束规划模型描述该不确定性，随后通过样本近似法计算云层覆盖不确定性下的观测收益。针对大规模观测任务，我们进一步提出了一种结合快速插入策略的改进型基于模拟退火的启发式算法。实验结果表明，在解决云层覆盖不确定性下的多颗AEOSs调度问题时，该改进模拟退火启发式算法的性能优于其他算法，验证了所提算法的效率与有效性。
英文摘要：
Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments equipped on satellites cannot see through the cloud, the cloud coverage has a significant influence on the satellite observation missions. We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit. The chance constraint programming model is adopted to describe the uncertainty initially, and the observation profit under cloud coverage uncertainty is then calculated via sample approximation method. Subsequently, an improved simulated annealing based heuristic combining a fast insertion strategy is proposed for large-scale observation missions. The experimental results show that the improved simulated annealing heuristic outperforms other algorithms for the multiple AEOSs scheduling problem under cloud coverage uncertainty, which verifies the efficiency and effectiveness of the proposed algorithm.
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