## 5 Data Simulation and Analysis
### Experimental Results and Analysis of Airport Gate Assignment  
In this paper, the proposed CG-TSA was run 20 times independently to solve the gate assignment problem. To assess the efficacy of the CG-TSA and the optimisation model, an optimal gate assignment system was used. Figure 5 depicts the Gantt chart of the optimal gate assignment results. The horizontal coordinate in Figure 5 represents the moment, the vertical coordinate the gate serial number, and the rectangle a designated flight. The corresponding flight, which is used to indicate the positioned flight, is marked on the aircraft number. The plan is visible for parking at Gates 1-10 with no overlap. The robust multiobjective optimisation model for maximising real-time flights for boarding gate assignments can handle dynamic changes such as flight conflicts, delays, and so on. The proposed CG-TSA solved the multiobjective optimisation model for the gate assignment problem quickly and efficiently. It demonstrated better optimisation capability in complex optimisation problems. \begin{table}
\begin{tabular}{c c c} \hline
**Gates** & **Type** & **Near the Boarding Bridge** \\ \hline Gate1 & large & 1 \\ Gate2 & medium & 1 \\ Gate3 & medium & 1 \\ Gate4 & small & 1 \\ Gate5 & medium & 2 \\ Gate6 & large & 1 \\ Gate7 & large & 1 \\ Gate8 & medium & 2 \\ Gate9 & large & 2 \\ Gate10 & medium & 2 \\ \hline \end{tabular}
\end{table} 
Table 7: Detailed information on gates. Figure 5: Gantt chart of airport gate assignment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Flight No.** &
\begin{tabular}{c} **Flight Serial** \\ **No.** \\ \end{tabular} & **Flight Type** & **Match Type** & **Landing Strip** \\ \hline  
\\ \hline \hline \end{tabular}
\end{table} 
Table 8: Detailed information on flights. \begin{table}
\begin{tabular}{c c} \hline \hline
**Flight Type** & **Fuel Consumption (kg/min)** \\ \hline Flight 1 & 500 \\ Flight 2 & 738 \\ Flight 3 & 928 \\ Flight 4 & 1506 \\ Flight 5 & 1850 \\ Flight 6 & 2438 \\ Flight 7 & 2627 \\ Flight 8 & 3137 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Detailed information on fuel consumption. Considering passenger convenience and satisfaction, as well as airport fuel costs, the boarding bridge rate and burning fuel costs were optimised as the objective functions. The GA [38], basic TSA, and CG-TSA were used to test and compare the objective optimisation model. The experimental results are shown in Figures 6 and 7. In Figure 6, the horizontal coordinate indicates the number of iterations, and the vertical coordinate indicates the boarding bridge rate. As shown in Figure 6, the basic TSA helped flights to stop at the boarding bridge with a boarding bridge rate of approximately 82% as the number of iterations increased. The GA commonly used for gate assignment achieved an approximately 91.5% bridging rate, and the proposed CG-TSA helped the bridging rate of flights to reach approximately 97.5%. Thus, the proposed CG-TSA in this paper had high optimisation efficiency and better search results than the traditional GA and basic TSA. The results obtained by CG-TSA were very close to the ideal solution to meet the needs of airport authorities. In Figure 7, the horizontal coordinate indicates the number of iterations, and the vertical coordinate indicates the total fuel consumption (kg). As shown in Figure 7, as a continuum of the iterative process, the convergence rate of the algorithm gradually decreased, and the minimised fuel consumption was continuously optimised. After 140 iterations, the basic TSA curve stabilised, and the minimum optimal value point of fuel consumption explored was approximately \(2.19\times 10^{6}\) kg. After 150 iterations, the conventional GA curve stabilised, and the minimised fuel consumption value reached was approximately \(1.95\times 10^{6}\) kg. The proposed CG-TSA stabilised after 190 iterations, and the achieved minimised fuel consumption value was approximately \(1.61\times 10^{6}\) kg.