## 5 Experimental Results and Analysis
### Dynamic Scheduling Parameters Analysis of Periodic RH Strategy  
In Section 3, the size of the RH window was discussed, which was closely related to the carrier aircraft scheduling problems, and had a great influence on the RH scheduling results. It was necessary to select the appropriate size of the RH window before the rolling mechanism was triggered. In order to get a better window size, we selected a different size of the time window for Instance 3. Each size of the time window in the periodic RH scheduling strategy was calculated 10 times in each experiment. We then recorded the window size (_WS_), roll times (_R_), makespan (_M_), each window's computational time (_WT_), the total computational time (_TT_), the average computational time (_AT_), and finally, each window's convergence gen (_CG_). Table 4 shows the optimum computational results. Table 4 shows the relationships between the window size and the computational results. When the time of the WS is too little, \(R\) increases. In each RH window, only a small portion of the activities are considered to get the local optimal scheduling. When the _WS_ was set as 15 min, the convergence results can be obtained in the first gen because of its small computation scales. The final results considered less global optimal scheduling. When the _WS_ increases, the number of activities involved in the waiting scheduling window increases, and the computational scale and time increased. A large rolling _WS_ can reduce the number of rolling windows. An excessive number of activities in a window could increase the computation time of a single window schedule, and the final convergence gen \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**WS (min)** & \(R\) & _M (min)_ & _WT (s)_ & _TT (s)_ & _AT (s)_ & _CG_ \\ \hline
15 & 5 & 77 & 12.92, 25.69, 20.98, 31.95, 2.37 & 93.91 & 18.78 & 1, 1, 1, 1 \\
20 & 4 & 72 & 24.19, 36.22, 41.65, 2.25 & 104.31 & 26.08 & 2, 5, 7, 10 \\
25 & 3 & 66 & 29.78, 49.56, 22.10 & 101.44 & 33.81 & 26, 35, 23 \\
30 & 3 & 67 & 46.08, 60.94, 2.14 & 109.16 & 36.39 & 19, 30, 3 \\
35 & 2 & 66 & 54.97, 51.92 & 106.89 & 53.45 & 42, 62 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of window size analysis. WT: each window’s computational time; TT: the total computational time; AT: the average computational time; CG: each window’s convergence gen.  
Figure 8: Gantt chart of optimal support equipment allocation in the static scheduling. also appeared later, but there was not an obvious variation in the total computational time. In order to achieve a more efficient dynamic schedule, the _WS_ as 25 min was selected to balance the rolling times (_R_) and each window's computational time (_WT_). Figure 9 shows the convergence curves in the different RH windows. Figure 9 shows that the periodic RH strategy can make the calculation easier to converge, because the number of waiting scheduling activities in a single RH window is reduced, which is less than the static scheduling. Different numbers of carrier aircraft were set to make a comparison and obtain the support operational results under the same conditions by the DPGA algorithm. Table 5 shows the comparison results with and without periodic RH scheduling for different waves of carrier aircraft. The experimental results in Table 5 show that the total computational time (_TT_) of the periodic RH dynamic scheduling is a little shorter than the static scheduling results. However, the computational time of each RH window (_WT_) decreased greatly. The reduced computational time makes the scheduling easier and more able to cope with the dynamic environment. It was found that the total computational time (_TT_), each window's computational time (_WT_), and finally the convergence gen (_CG_) of the three experiments obtained from the proposed approach were better than those obtained from the static rolling horizon approach. The global optimal scheduling may be lost by the RH dynamic scheduling strategy. Moreover, the makespan and computational time of the proposed approach was acceptable.