## 4 Results and Discussion
### Case Study for DMSA in Dynamic Scheduling
#### 4.2.3 Simulation Results Analysis
After dynamic scheduling, 91.4% of targets are observed with only 10.3% change to the initial plan, 89.3% of priorities are executed, 75% of emergency targets are allocated satellite resources, and the evaluation function value is 0.86. The number of targets executed by optical satellites (S2, S5, and S6) are 4, 3, 4, while the number of SAR satellites (S1, S3, and S4) are 8, 6, 7. It indicates that missions are allocated in a balanced way among satellites; the scheduling result is shown in Figure 8. It is noteworthy that unscheduled targets are all visible light targets, and during the whole scheduling period, Target17 and Target31 cannot be observed by any optical satellites. Moreover, because the Walker constellation is on the sun-synchronous orbit, Target30 can only be observed by S2 for once. Consequently, missed observing opportunity in the initial plan will lead to failure of observation after dynamic scheduling. Therefore, the result shows that the algorithm can efficiently make adjustments after an emergency happens and guarantee the stability of the initial scheduling plan. Targets with high priority and sufficient observation windows will be scheduled with satisfactory effect. Several more experimental cases are conducted in this section to verify the effectiveness of the proposed method. Six scenarios with different satellites and resources are setup, and the evaluation function defined in Section 3.4.3 is performed to evaluate the scheduling results. Computational complexity is measured by the elapsed CPU time. Detailed evaluation results are shown in Table 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target ID** & **Priority** & **Satellite ID** & **Start Time** & **End Time** \\ \hline Target1 & 4 & S4 & 0:33:27 & 0:37:36 \\ Target2 & 4 & S4 & 14:42:52 & 14:49:05 \\ Target3 & 4 & S5 & 22:48:53 & 22:51:50 \\ Target4 & 3 & S3 & 2:08:03 & 2:13:17 \\ Target5 & 4 & S3 & 17:52:24 & 17:58:03 \\ Target6 & 5 & S5 & 0:33:25 & 0:36:08 \\ Target7 & 4 & S1 & 10:22:50 & 10:29:23 \\ Target8 & 2 & S4 & 9:01:19 & 9:07:56 \\ Target9 & 3 & S4 & 12:48:30 & 12:54:09 \\ Target10 & 3 & S1 & 4:06:20 & 4:12:45 \\ Target11 & 5 & S6 & 17:23:42 & 17:26:11 \\ Target12 & 4 & S3 & 23:12:30 & 23:15:20 \\ Target13 & 2 & S5 & 16:01:22 & 16:04:26 \\ Target14 & 4 & S4 & 9:58:56 & 10:01:40 \\ Target15 & 2 & S1 & 17:45:33 & 17:51:46 \\ Target16 & 4 & S1 & 18:19:39 & 18:25:49 \\ Target17 & 4 & unscheduled & / & / \\ Target18 & 2 & S2 & 14:42:15 & 14:44:01 \\ Target19 & 1 & S6 & 17:05:26 & 17:07:41 \\ Target20 & 5 & S3 & 19:32:21 & 19:38:55 \\ Target21 & 3 & S2 & 11:32:14 & 11:33:18 \\ Target22 & 2 & S4 & 11:59:12 & 12:05:39 \\ Target23 & 2 & S3 & 13:29:07 & 13:35:10 \\ Target24 & 4 & S1 & 9:48:14 & 9:49:59 \\ Target25 & 4 & S6 & 18:43:41 & 18:46:45 \\ Target26 & 2 & S1 & 12:18:22 & 12:20:52 \\ Target27 & 1 & S3 & 2:21:50 & 2:24:20 \\ Target28 & 3 & S6 & 7:44:52 & 7:47:51 \\ Target29 & 2 & S2 & 22:53:34 & 22:56:37 \\ Target30 & 4 & S2 & 14:34:52 & 14:37:03 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Initial scheduling plan. It shows that as the scale of the scenarios increases, the elapsed CPU times of both regular and dynamic scheduling exhibit an increasing trend. The average _CR_ of 0.953 indicates our method can schedule most of the targets in the scenarios. It is noteworthy that _PR_ is always higher than CR, and the average ER is 0.948, both of which indicate that targets with high priority are more likely to be scheduled. The _IR_ is related to the proportion of emergency targets to initial targets, we can see that in the 3 rd scenario where the proportion is the largest (\((10+20)/50\)\(\times\) 100% = 60%), the change to initial plan after dynamic scheduling is also the largest. The running times of dynamic scheduling are much shorter than those of static scheduling, and this is because the strategy in ATSA will trim the searching space which leads to a quicker convergence.