Any reliable model for scheduling optical space-to-ground communication must factor in cloud cover conditions due to attenuation of the laser beam by water droplets in the clouds. In this article, we provide two alternative models of uncertainty for cloud cover predictions: a robust optimization model with a polyhedral uncertainty set and a distributionally robust optimization model with a moment-based ambiguity set. We computationally analyze their performance over a realistic communication system with one satellite and a network of ground stations located in the U.K. The models are solved to schedule satellite operations for six months utilizing cloud cover predictions from official weather forecasts. We found that the presented formulations with the treatment of uncertainty outperform in the long-term models, in which uncertainty is ignored. Both treatments of uncertainty exhibit similar performance. Nonetheless, the novel variant with the polyhedral uncertainty set is considerably faster to solve.

中文翻译：

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云覆盖不确定性下的空地光通信调度

由于云中的水滴会衰减激光束，因此任何用于调度光学空地通信的可靠模型都必须考虑云覆盖条件。在本文中，我们为云层预测提供了两种可选的不确定性模型：具有多面体不确定性集的稳健优化模型和具有基于矩的模糊集的分布稳健优化模型。我们通过具有一颗卫星和位于英国的地面站网络的现实通信系统对它们的性能进行计算分析。利用官方天气预报的云量预测，解决模型以安排卫星运行六个月。我们发现所提出的带有不确定性处理的公式在长期模型中表现优于忽略不确定性的长期模型。不确定性的两种处理表现出相似的性能。尽管如此，具有多面体不确定性集的新变体的求解速度要快得多。