## 2 Related Works  
This section addresses different formulations and applications of the scheduling problem on multi-satellite mission optimization. Here, the following aspects of interest are observed: the main purpose/application; whether the formulation has power constraintsand/or deals with multiple objectives; how the multi-satellite dynamic is approached; and what method is used for solving the scheduling problem. Earth observation (EO) missions represent a typical application field of task scheduling. [PERSON] and [PERSON] [PERSON] and [PERSON] (2019) propose a dynamic scheduling model based on mission priority for emergency earth observation with multiple satellites, which allows the admission of newly arrived tasks. This multi-objective application is energy constrained and targets: the maximization of the sum of mission priorities, maximization of total mission revenues, and minimization of waiting time for missions requiring urgent execution. The solution is obtained through a hybrid genetic tabu search algorithm; therefore, it is not precise. Moreover, EO with energy constraints and multiple objectives are addressed by [PERSON] et al. [PERSON] et al. (2019). In their work, similar to [PERSON] and [PERSON] (2019), there is only one solution--not a set, i.e., the objectives are not mutually exclusive--and it is also not obtained through an exact algorithm; instead, the authors propose a heuristic hierarchical scheduling method based on ant colony. An exact MILP formulation is presented by [PERSON] et al. [PERSON] et al. (2019) for EO applications but with no energy constraints or multiple-objective considerations. Data transfer is another commonly addressed issue. [PERSON] et al. [PERSON] et al. (2019) suggest an iterative graph-based heuristic for maximizing data downloading from Ground station within its contact window with the multi-satellite system. Only download and offload times are considered resources; therefore, the model is not energy constrained. Data transfer and battery usage are both optimized in the work of [PERSON] et al. (2019), but the presented approach is not easily generalizable since it is based on a specific CubeSat constellation from the Danish mission called Ulloriaq. [PERSON] (2015) created a mixed-integer scheduling model within a set of possible events for a constellation of imaging satellites that did not use a heuristic solving method. His linear model enabled a better understanding of the performance of the simulated annealing solver and could also be adapted to different real-world scheduling problems. Each satellite could have different events with diverse utilities and costs in the model. The results showed the possibility to model the scheduling problem for a constellation of up to 30 satellites as a mixed-integer linear model, and the model could be improved to the point of being more time-efficient than a simulated annealing process. The work of [PERSON] et al. [PERSON] et al. (2019) proposed a mixed-integer linear program formulation for the job scheduling of a constellation and investigated the applicability/scalability of a standard MILP solver that produces the optimal solution. The goal of the satellite constellation job scheduling was to allocate each job for satellites and determine the job starting times to maximize overall mission performance. The constraints included visibility time windows, transition time between consecutive jobs, maximum attitude angle, energy capacity, and memory capacity. The formulation relaxed some of the constraints, which would not impact real instances, but also included precedence conditions between jobs and job-agent compatibility constraints. An off-the-shelf MILP solver was used to obtain the optimal solution for the scheduling formulation and numerical experiments indicated that the optimal solution can be obtained in a tractable manner up to the problem size with three satellites and hundreds of jobs.