## 4 Proposed Algorithm
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **SA** & **LS** & **VNS** \\ \hline \multirow{3}{*}{\(r\)} & 1000 m & 5.647 & 9.136 & 3.686 \\  & 2000 m & 3.978 & 6.344 & 2.394 \\  & 3000 m & 3.084 & 5.212 & 1.307 \\ \hline \multirow{3}{*}{\(m\)} & 5 & 3.891 & 9.235 & 2.389 \\  & 10 & 3.376 & 7.618 & 1.915 \\  & 15 & 2.409 & 5.758 & 1.508 \\ \hline \multirow{3}{*}{\(g\)} & 3 kg & 4.364 & 7.791 & 2.433 \\  & 4 kg & 3.862 & 6.179 & 1.884 \\  & 5 kg & 3.649 & 5.537 & 1.819 \\ \hline \multirow{3}{*}{\(G\)} & 80 kg & 3.416 & 5.097 & 1.682 \\  & 100 kg & 4.183 & 6.654 & 2.253 \\  & 120 kg & 4.878 & 7.086 & 3.173 \\ \hline \multirow{3}{*}{\(h\)} & 3 & 3.907 & 6.103 & 2.416 \\  & 4 & 4.084 & 6.313 & 2.589 \\ \cline{1-1}  & 5 & 4.196 & 6.657 & 2.603 \\ \hline \multicolumn{3}{c}{Average} & 3.928 & 6.715 & 2.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ARPD values. Figure 6: Interactions between \(g\), \(G\) and \(h\) with 95.0% Tukey HSD intervals. Figure 8 is the path planning diagram of UAV\({}_{1}\) based on the map data and the calculation results shown in Figure 7. UAV\({}_{1}\) has six missions in total. The first one: it starts from \(s_{0}\), passes \(s_{3}\), \(s_{5}\) and \(s_{1}\) in turn, and then returns to \(s_{0}\). The second one and the third one are both missions between \(s_{0}\) and \(s_{2}\). The fourth one is shuttle flights between \(s_{0}\) and \(s_{5}\). The fifth one is flight from \(s_{0}\), passes \(s_{5}\) and \(s_{4}\) in turn, and then returns to \(s_{0}\). The sixth one is from \(s_{0}\), passes \(s_{3}\) and \(s_{5}\) in turn, and then returns to \(s_{0}\). Among them, the reason that the same destination is distributed twice is that packages delivered exceed the upper load limit of the UAV. Figure 9 is the path planning diagram of UAV\({}_{2}\) based on the map data and the calculation results shown in Figure 7. As shown in Figure 9, UAV\({}_{2}\) has seven missions in total. The first and the third missions both start from \(s_{0}\), pass \(s_{2}\) and \(s_{5}\) in turn, and then return to \(s_{0}\). The second one and the fourth one's missions are between \(s_{0}\) and \(s_{5}\). The fifth one is flight from \(s_{0}\), passes \(s_{3}\), \(s_{5}\), \(s_{4}\), and \(s_{1}\) in turn, and then returns to \(s_{0}\). Figure 8: Routes of UAV\({}_{1}\). Figure 7: Small scale experimental results of UAV path planning. Figure 10 is the path planning diagram of UAV\({}_{3}\) based on the map data and the calculation results shown in Figure 7. As shown in Figure 10, UAV\({}_{3}\) has seven missions in total. The first, fourth, and fifth missions are flights between \(s_{0}\), and \(s_{5}\). The second and third missions are shuttle flights between \(s_{0}\) and \(s_{2}\). The sixth one is flight from \(s_{0}\), passes \(s_{2}\), \(s_{5}\), and then returns to \(s_{0}\). The seventh one is flight from \(s_{0}\), passes \(s_{5}\), \(s_{4}\), and then returns to \(s_{0}\). Since the considered problem is biobjective, we apply a linear weighted sum (LWS) method to evaluate the solutions on both the total completion time and the satisfaction perspectives, as defined as Equation (14). \(\omega\) and \(1-\omega\)\((\omega\in(0,1))\) are the weight according to the importance of the total completion time and the satisfaction, respectively. Therefore, the smaller the LWS, the better the solution will be. \[\text{LWS}=\frac{A-A_{min}}{A_{min}}\omega+\frac{B-B_{min}}{B_{min}}(1-\omega) \tag{14}\]

As can be seen from Figure 11, compared with no difference shown in (c), when the weight of customer satisfaction \(A\) is higher and the weight of total completion time \(B\) is lower, the Relative Percentage Deviation (RPD) value of Local Search (LS) algorithm is larger and the RPD value of variable neighborhood search (VNS) algorithm is smaller shown in (e). Conversely, the RPD value of LS algorithm is smaller and the RPD value of VNS algorithm is larger, shown in (a).