## 4 Computational Experiments and Analyses
### Experiments of Different Regulatory Factors  
In the freshness loss coefficient function, the time-sensitive regulating factor \(r\) corresponds to the perishability of different fresh products. In order to test the impact of different values of \(r\) on the cost and distribution plan, the data of the first 50 customers in the R202 data set was used in this experiment. The value of \(r\) is between 0.1 and 0.9, and the step size of the value is 0.1. The experimental results are shown in Tables 4 and 5. DT represents the departure time of the vehicle from the distribution center, and RT represents the return time of the vehicle to the distribution center. It can be seen from Table 4, Figure 8, and Table 5 that: (1) with the increase of r value, the total cost, freshness loss cost, and the proportion of freshness loss in the total cost all decreased to different degrees. Compared with the maximum value, the lowest value of total cost decreased by 33.6%, the lowest value of freshness loss cost decreased by 72.4%, and the proportion of freshness loss decreased by 58.5%. This indicates that freshness loss on the total cost will gradually decrease with the decrease of the time-sensitivity of fresh products. (2) It can also be concluded from the degree of inclination of the curve that when the value of r is small, the cost decreases greatly with each 0.1-point increase in the value of r; when the value of r is large, the cost decreases slightly with each 0.1-point increase in the value of r. (3) According to the distribution plans corresponding to different r values, although the number of vehicles in each distribution plan is equal, the distribution vehicle paths will be significantly different. When \(r=0.9\), the delivery route of the first car is the longest among all the distribution plans, and the number of customers to be delivered reaches 26. The delivery route of the third car is the shortest among all the distribution plans, with only nine customers. The reason for this significant difference is that each customer has a different optimal service time window. In order to reduce the penalty cost of the time window, vehicles need to try to reach the customer within the optimal service time. (4) From the departure and return time, it can be seen that the vehicle departure time from the distribution center is not limited to 0. However, the best departure time point is selected according to the customer time window and time-varying road network conditions. The vehicle return time also meets the time window requirements of the distribution center. Based on the above analysis, it can be seen that enterprises \begin{table}
\begin{tabular}{c c c c} \hline \hline
**r** & **TC** & **FL** & **FL/TC** \\ \hline
0.1 & 12,573.78 & 3030.28 & 0.241 \\
0.2 & 11,143.33 & 2529.54 & 0.227 \\
0.3 & 10,339.7 & 1985.22 & 0.192 \\
0.4 & 9752.06 & 1833.39 & 0.188 \\
0.5 & 9375.41 & 1546.94 & 0.165 \\
0.6 & 9107.99 & 1256.90 & 0.138 \\
0.7 & 8928.38 & 1053.55 & 0.118 \\
0.8 & 8840.07 & 919.37 & 0.104 \\
0.9 & 8834.48 & 865.78 & 0.098 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The impact of different regulatory factors on cost. Experiments to Analyze the Relationship among Distribution Cost, Carbon Emission Cost, and Freshness Loss  
In order to analyze the relationship among distribution cost, carbon emission cost, and freshness loss, these three costs were assigned weight \(\omega_{1}\), \(\omega_{2}\) and \(\omega_{3}\), respectively, where \(\omega_{1}+\omega_{2}+\omega_{3}=1\), \(0<\omega_{1}<1\), \(0<\omega_{2}<1\), \(0<\omega_{3}<1\), and the weight step is 0.1. In order to emphasize the importance of carbon emission and ensure that the weight of distribution cost and freshness loss is not 0, let \(0.5\leq\omega_{2}<0.9\). The first 50 customer data points from the R202 dataset were selected for the experiment. The experimental results are shown in Table 6. DC represents distribution cost and WV represents weighted objective function value. All costs units are yuan.