## 6 Conclusions  
This study reports the results of an investigation of the AEOS scheduling problem. An MDP model was constructed to illustrate the scheduling process problem. In MDP, the environment was described by two categories of attributes, original and direct attributes, based on which we designed an ADN model. With the input of the attribute data, the ADN model determined the task scheduling sequence. After a feasible time interval was defined, the tasks were arranged to start at the earliest time of the feasible time intervals. We also proposed ADN_LS, which is a combination of ADN and the proposed start-time-shift-based local search. A comprehensive experiment was conducted to evaluate the performance of the proposed method. The computational results showed that the attention mechanism in our ADN was beneficial for the training process to converge to better strategies. The proposed local search method can improve the quality of the ADN solutions. Compared to the two advanced algorithms (ALNS and DRL), our method obtained a better total profit in the test \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{3}{c}{**ADN\_LS**} & \multicolumn{3}{c}{**ADN**} & \multicolumn{3}{c}{**ALNS**} & \multicolumn{3}{c}{**DRL**} \\ \cline{2-13}  & **Max** & **Min** & **Range** & **Max** & **Min** & **Range** & **Max** & **Min** & **Range** & **Max** & **Min** & **Range** \\ \hline
100 & 1 & 1 & 0 & 1 & 1 & 0 & 1 & 1 & 0 & 1 & 1 & 0 \\
200 & 0.9749 & 0.9189 & 0.0560 & 0.9749 & 0.9189 & 0.0560 & 0.9693 & 0.9180 & 0.0513 & 0.9367 & 0.8631 & 0.0737 \\
300 & 0.7996 & 0.7755 & 0.0242 & 0.7996 & 0.7736 & 0.0260 & 0.7907 & 0.7276 & 0.0651 & 0.7663 & 0.7091 & 0.0572 \\
400 & 0.7288 & 0.6604 & 0.0384 & 0.7288 & 0.6904 & 0.0384 & 0.7213 & 0.6544 & 0.0669 & 0.6916 & 0.6313 & 0.0603 \\
500 & 0.6233 & 0.6103 & 0.0150 & 0.6253 & 0.6103 & 0.0150 & 0.6247 & 0.6025 & 0.0222 & 0.5789 & 0.5131 & 0.0658 \\
600 & 0.5542 & 0.5464 & 0.0078 & 0.5539 & 0.5445 & 0.0094 & 0.5623 & 0.5221 & 0.0402 & 0.4678 & 0.4529 & 0.0148 \\
700 & 0.5003 & 0.4750 & 0.0253 & 0.4995 & 0.4734 & 0.0261 & 0.4987 & 0.4794 & 0.0193 & 0.4573 & 0.4257 & 0.0316 \\
800 & 0.4577 & 0.4257 & 0.0320 & 0.4573 & 0.4257 & 0.0316 & 0.4503 & 0.4168 & 0.0335 & 0.3651 & 0.3331 & 0.0320 \\
900 & 0.4180 & 0.3811 & 0.0369 & 0.4180 & 0.3800 & 0.0380 & 0.4073 & 0.3738 & 0.0336 & 0.3356 & 0.3051 & 0.0305 \\
1000 & 0.3884 & 0.3465 & 0.0418 & 0.3880 & 0.3460 & 0.0420 & 0.3771 & 0.3536 & 0.0235 & 0.3201 & 0.2627 & 0.0573 \\ \hline \hline \end{tabular} Max represents the maximum value; Min represents the minimum value. The largest maximum value and the smallest range are marked with underlining.  
\end{table} 
Table 9: Maximum, minimum, and range of PAR results. sets. According to the test results, our method exhibited considerable time efficiency for the AEOSSP (within 18 s for up to 1000 tasks). Owing to the superior time efficiency and effective problem-solving capabilities, the proposed ADN and ADN_LS can be extended in future studies in the following directions:  
* The proposed methods can be applied as a rescheduling strategy for real-time scheduling scenarios, including emergency scheduling, autonomous task scheduling, and uncertain scenarios;
* In combination with preprocessing techniques, the proposed methods can be applied to the AEOS scheduling scenarios with complex targets. Complex targets, such as area targets and mobile targets, are disaggregated into atom-tasks using preprocessing techniques. The atom tasks can be scheduled using the proposed methods after the determination of their original and direct attributes;
* Another promising direction involves combining the proposed methods with distributed frameworks to explore their application under distributed multi-AEOS scheduling scenarios. In distributed algorithm frameworks, remote sensing tasks are typically allocated to different AEOSs, followed by their scheduling on a single satellite.