Efficient utilization of satellite resources in dynamic environments remains
a challenging problem in satellite scheduling. This paper addresses the
multi-satellite collection scheduling problem (m-SatCSP), aiming to optimize
task scheduling over a constellation of satellites under uncertain conditions
such as cloud cover. Leveraging Monte Carlo Tree Search (MCTS), a stochastic
search algorithm, two versions of MCTS are explored to schedule satellites
effectively. Hyperparameter tuning is conducted to optimize the algorithm's
performance. Experimental results demonstrate the effectiveness of the MCTS
approach, outperforming existing methods in both solution quality and
efficiency. Comparative analysis against other scheduling algorithms showcases
competitive performance, positioning MCTS as a promising solution for satellite
task scheduling in dynamic environments.

在动态环境中，高效利用卫星资源仍然是卫星排程中的一个具有挑战性的问题。本文解决了多卫星集合排程问题（m-SatCSP），旨在优化在云覆盖等不确定条件下对卫星星座进行任务排定。通过使用蒙特卡洛树搜索（MCTS），探索了两个版本的 MCTS 来有效地安排卫星。进行超参数调整以优化算法的性能。实验结果表明了 MCTS 方法的有效性，在解决质量和效率方面均优于现有方法。与其他排程算法的比较分析展示了有竞争力的表现，将 MCTS 定位为动态环境卫星任务排程的一个有前景的解决方案。