Optimal Scheduling of Imaging Missions for Multiple Satellites Using Linear Programming Model

Efficient scheduling of multiple missions for Earth-monitoring satellites contributes to an essential upgrade in the optimality of spacecraft observation systems for lower Earth orbit (LEO). Nowadays, tasks of a satellite are getting more complex due to advances in the satellite's operating and controlling mechanisms. As a result of these changes, the satellite's mission scheduling problem needs to consider its parametrical properties and computing resources. Since it is challenging to consider various mission constraints and parameters manually, it is necessary to design an effective optimization model for scheduling problems. For this purpose, we proposed a new mixed-integer linear programming (MILP) strategy that utilizes a branch and bound (BB) method to compute satellites' imaging mission scheduling. The proposed optimization model maximizes the value of an objective function considering different factors such as satellite resource consumption and user-request priority. In addition, to validate the effectiveness of the proposed strategy, we assumed complex imaging missions for satellite control systems containing various satellites with multiple operational modes. The simulation considered three different operating modes for satellites (i.e., 'general mode,' 'commercial mode,' and 'tactical mode') to confirm the efficiency of the proposed optimization model. As a result, the proposed strategy provides an optimal solution with an improved computational time. Finally, the developed model provides a better result compared to a previously used suboptimal metaheuristic genetic algorithm.