Benders decomposition for the energy aware task scheduling of constellations of nanosatellites

This paper describes a novel method for optimizing nanosatellite constellation scheduling based on Benders decomposition. The optimization of nanosatellite constellations is challenging due to the large number of variables and constraints involved, which increases the computational work required to get optimal solutions as the number of participating objects increases. Our strategy overcomes this obstacle by decomposing the complex problem into smaller, more manageable subproblems, rendering the original formulation scalable. The outcomes demonstrate that the Benders decomposition achieves the same optimal solutions obtained with the Gurobi solver applied to the baseline formulation while being significantly faster and more efficient, especially for larger constellation sizes. This method provides a valuable tool for decision-makers in the nanosatellite job scheduling process, highlighting the potential benefits of using decomposition methods such as the Benders decomposition for large-scale or challenging optimization problems in Offline Nanosatellite Task Scheduling (ONTS).