## 4 Simulation Results  
In this section, we compare the calculation results and time taken for different scheduling methods including RS, OPH [34; 35], DPH, and EPH. At the same time, we also included the tabu search algorithm (TS) [36; 37] as a conventional meta-heuristic scheduling method for comparison. We constructed four neighborhoods for the tabu search algorithm including the scheduled mission exchange neighborhood, unscheduled mission and scheduled mission exchange neighborhood, and the unscheduled mission insertion neighborhood and scheduled mission deletion neighborhood. We then used the satellite parameters shown in Table 2 to form the simulation conditions for a comparison between the different scheduling methods; these satellite parameters can be found in [38]. Table 2 shows the orbital elements without the true anomaly and we set the true anomaly value to 120\({}^{\circ}\) as the satellite's initial position in all simulation conditions. The scheduling period was set to an orbital period of 5800 s. Three groups of simulation conditions were constructed, the first group of conditions contained a satellite that had enough data transmission resources and initial electrical energy, the second group of conditions contained a satellite that had sufficient initial electrical energy but was short of data transmission resources, and finally, the third group of conditions contained a satellite with enough data transmission resources but lacked the initial electrical energy. Each group contained ten conditions in which randomly distributed target points were assigned random values within a given range. The condition parameters are shown in Table 3.  
The calculation program was executed on a computer with an intel Core [EMAIL_ADDRESS] GHz CPU. The scheduling result of the OPH was used as the initial scheduling result for the tabu search algorithm. The maximum number of iterations of the tabu search algorithm was set as 500. When the historical optimal value was achieved, the calculation time taken was recorded. The coefficients in Equations (22), (24), and (25) were \(c_{o}=1\), \(c_{d}=1\), and \(c_{e}=1\). The comparison of the scheduling results of the three groups of conditions are shown in Figure 7, and the results are listed in Table 4. It is obvious that TS achieved the best scheduling results among all of the methods under different conditions. The objective function value \(J\) of TS then served as a baseline for comparison with other methods. From the first group of results, it was noticeable that OPH and RS had better results when the data transmission time windows, memory capacity, and electrical energy were enough, the objective function value \(J\) was 96% of TS, and the results of DPH and EPH were worse as both had the objective values \(J\) of 88% and 74% of TS. Moreover, it can be seen from the second group of results that DPH and RS had better results when the data transmission time windows were short, and their objective function value \(J\) was 98% of TS, while the results of OPH and EPH were worse in which the objective values \(J\) were 88% and 80% of TS. From the third group of results, EPH and RS clearly had better results when the electricity energy was low as the objective function value \(J\) was 92% of TS. In contrast, the results of OPH and EPH were worse as their objective values \(J\) were 74% of TS. The calculation time of different methods in the three groups of conditions are shown in Table 5. Since TS is an iterative algorithm, the calculation time taken is much longer than RS and the heuristic scheduling methods. The average number of iterations for TS to achieve the optimal solution in the three group of conditions was 87, 239, and 249, respectively. A single condition from each of the three groups of conditions was chosen for analysis.