This paper investigates a novel problem, namely the Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP). Unlike the static AEOSSP, it takes into account a range of uncertain factors (e.g., task profit, resource consumption, and task visibility) in order to reflect the reality that the actual information is inherently unknown beforehand. An effective Genetic Programming Hyper-Heuristic (GPHH) is designed to automate the generation of scheduling policies. The evolved scheduling policies can be utilized to adjust plans in real time and perform exceptionally well. Experimental results demonstrate that evolved scheduling policies significantly outperform both well-designed Look-Ahead Heuristics (LAHs) and Manually Designed Heuristics (MDHs). Specifically, the policies generated by GPHH achieve an average improvement of 5.03% compared to LAHs and 8.14% compared to MDHs.

翻译：本文研究了一个新颖的问题，即不确定敏捷对地观测卫星调度问题。与静态的敏捷对地观测卫星调度问题不同，该问题考虑了一系列不确定因素（例如，任务收益、资源消耗和任务可见性），以反映实际信息本质上是预先未知的现实。本文设计了一种有效的遗传编程超启发式方法，用于自动化生成调度策略。演化出的调度策略可用于实时调整计划，并表现出卓越的性能。实验结果表明，演化出的调度策略显著优于精心设计的向前看启发式方法和人工设计的启发式方法。具体而言，由遗传编程超启发式方法生成的策略，与向前看启发式方法相比平均提升了5.03%，与人工设计的启发式方法相比平均提升了8.14%。