Graph neural networks for the offline nanosatellite task scheduling problem

Abstract

This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNNs). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the goal is to find the optimal schedule for tasks to be carried out in orbit while taking into account Quality-of-Service (QoS) considerations such as priority, minimum and maximum activation events, execution time-frames, periods, and execution windows, as well as constraints on the satellite's power resources and the complexity of energy harvesting and management. This study explores the use of Graph Neural Networks (GNNs) as primal heuristics for the ONTS problem, as this class of deep learning models has been effectively applied to optimization problems such as the traveling salesman, scheduling, and facility placement. We investigate whether GNNs can learn the complex structure of the ONTS problem with respect to feasibility and optimality of candidate solutions. Furthermore, we evaluate using GNN-based heuristic solutions to provide better solutions (w.r.t. the objective value) to the ONTS problem and reduce the optimization cost. Our experiments show that GNNs are not only able to learn feasibility and optimality for instances of the ONTS problem, but they can generalize to harder instances than those seen during training, addressing the data acquisition cost of training deep learning models on optimization problems. On top of that, the GNN-based heuristics improved the expected objective value of the best solution found under the time limit by 45 %, and reduced the expected time to find a feasible solution by 35 %, when compared to the SCIP (Solving Constraint Integer Programs) solver in its off-the-shelf configuration.