## 5 Experiments and Results
### Example Description and Result Analysis  
This paper takes SF Jinzhou Company as an example to analyze the problem of urban logistics site selection-path optimization. SF Jinzhou's urban logistics network is divided into urban distribution center, SF Express sales department, and terminal demand point, and SF Express business department is equivalent to the urban transit center in this model. The sales department has 3-15 delivery staff; the delivery staff fixed the distribution zone, and the distribution zone covers several terminal demand points. Terminal demand points include types such as delivery points, smart self-pickup lockers, and local merchant cooperation points. SF Jinzhou currently has 336 terminal demand points in the main urban area, including 11 standard parcel transit centers, 3 bulky parcel transit centers, and 1 city distribution center, of which the urban distribution center is responsible for air and land transportation, respectively. Figure 8 shows the distribution of existing transit centers of SF Jinzhou Company. In daily life, schools and residential areas are mainly standard parcels, while factories, industrial parks, and business districts are mainly large parcels. SF Jinzhou's models include vans, trucks, electric tricycles, and two-wheelers. The distribution of terminal demand points is highly dense and the distribution frequency is high, and logistics models with small capacity and strong mobility are mostly adopted. Five-ton trucks are used from the urban distribution center to each transit center, electric tricycles are used for standard parcels from the transit center to the terminal demand point, and vans are used for large parcels. According to SF Jinzhou Company's existing transit center and poten \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Alternate Points** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Demand** \\ **Points** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Threshold** \\ **(kg)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **The Objective** \\ **Function Value** \\ **(RMB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Elapsed** \\ **Time (s)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Threshold** \\ **(kg)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **The Objective** \\ **Function Value** \\ **(RMB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Elapsed** \\ **Time (s)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Flapsed** \\ **Time (s)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Flapsed** \\ **Error (\%)** \\ \end{tabular} } \\ \hline \multirow{3}{*}{2} & 10 & 2 & 2751.72 & 0.517 & 2 & 2621.87 & 0.532 & 0.95 \\  & 15 & 2 & 2976.98 & 0.782 & 2 & 2876.91 & 1.365 & 0.81 \\  & 20 & 2 & 3112.76 & 1.873 & 2 & 3091.83 & 31.276 & 1.91 \\ \hline \multirow{3}{*}{3} & 10 & 2 & 3087.09 & 0.687 & 2 & 3079.88 & 0.754 & 0.05 \\  & 15 & 2 & 3221.87 & 0.853 & 2 & 3228.21 & 78.376 & 0.15 \\  & 20 & 2 & 3412.91 & 1.138 & 2 & 3409.32 & 932.76 & 0.52 \\ \hline \multirow{3}{*}{4} & 10 & 2 & 3076.19 & 0.737 & 2 & 3082.23 & 3.873 & 0.40 \\  & 15 & 2 & 3298.13 & 0.962 & 2 & 3184.54 & \textgreater{}1800 & 0.44 \\ \cline{1-1}  & 20 & 2 & - & 1.238 & 2 & - & \textgreater{}1800 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Result comparison between the proposed algorithm and CPLEX. tial optional points, 76 transfer center candidate points are generated, and the delivery and collection of parcels at 336 terminal demand points are collected. The distance between nodes is obtained through Datamap, and the relevant parameter settings are shown in Table 6.  
According to the data in the above table, this article uses the Python 3.7.3 programming language to implement the above optimization algorithm, and the running platform is Intel Core i7-6700 HQ CPU @ 2.60 GHz, Win10 PC.