  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.
