CubeSats are small and affordable satellite platforms that are challenging to develop and operate due to their limited power and communication capabilities. Effective task scheduling is crucial to managing the limited power resources available on CubeSats, but this often involves solving large mixed-integer programming problems that are computationally intensive and time-consuming. To address this challenge, researchers propose a branch-cut-and-price algorithm that improves upon previous approaches in the literature by incorporating dual stabilization, new valid inequalities, parallel dynamic programming, and a new branching strategy based on pseudocosts.

The Offline Nanosatellite Task Scheduling (ONTS) problem aims to maximize task execution while adhering to Quality of Service constraints. Existing methods rely on branch-and-bound algorithms, but this work introduces the Column-Generation-based Genetic Algorithm (CGbGA) as a heuristic approach. This method combines Genetic Algorithm and Dynamic Programming to solve the problem efficiently, avoiding the computational burden of branch-and-bound. Results show the potential of CGbGA in terms of solution value and computational solving time compared to commercial solvers.