## 5 Discussion  
According to the above experimental results and the verification of previous studies, the most valuable findings are discussed below.  
1. According to previous studies, soft time windows have many advantages over traditional hard time windows [34]. The penalty cost setting of broken line soft time windows is closer to the actual feeling of customers [18]. This paper further verifies that the proposed approach can obtain better solutions and is more efficient compared with the hyper-heuristic genetic algorithm through the comparison experiment of the broken line soft time windows model. The proposed approach is more suitable for the broken line soft time windows model.
2. In the TDGVRPSTW model, the optimal solution found by the proposed approach was better than AGA and HGA, and the number of iterations was less, so the proposed approach is more efficient in optimizing. The literature [26] has proved that a systematic change of neighborhood structure can optimize the search of the solution  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(\omega_{1}\) & \(\omega_{2}\) & \(\omega_{3}\) & **DC** & **CE** & **FL** & **TC** & **WV** \\ \hline
0.1 & 0.5 & 0.4 & 7188.21 & 1053.29 & 2058.83 & 10,300.33 & 2069 \\
0.1 & 0.6 & 0.3 & 7206.43 & 1034.51 & 2070.57 & 10,311.51 & 1962.52 \\
0.1 & 0.7 & 0.2 & 7213.84 & 1012.65 & 2100.61 & 10,327.1 & 1850.36 \\
0.1 & 0.8 & 0.1 & 7194.61 & 998.27 & 2141.15 & 10,334.03 & 1732.19 \\
0.2 & 0.5 & 0.3 & 7100.59 & 1061.71 & 2079.87 & 10,242.17 & 2574.93 \\
0.2 & 0.6 & 0.2 & 7092.37 & 1048.93 & 2107.28 & 10,248.58 & 2469.29 \\
0.2 & 0.7 & 0.1 & 7095.68 & 1019.11 & 2142.78 & 10,257.57 & 2346.79 \\
0.3 & 0.5 & 0.2 & 6964.8 & 1073.49 & 2079.87 & 10,118.16 & 3042.16 \\
0.3 & 0.6 & 0.1 & 6985.66 & 1034.51 & 2129.81 & 10,149.98 & 2929.39 \\
0.4 & 0.5 & 0.1 & 6767.45 & 1030.93 & 2115.12 & 9913.5 & 3433.96 \\ \hline \end{tabular}
\end{table}
Table 6: Experimental results of different weight combination of distribution cost, carbon emission cost, and freshness loss.  
space. This paper experiments with the sorting of the optimal solutions of three algorithms and shows that the diversity of neighborhood structure is very important for search results and efficiency, and the more diverse the neighborhood structure, the better the results and efficiency.
3. According to previous studies [6], the geographical distribution of customers will have a great impact on the distribution cost. Through experiments on standard data sets, this paper further found that the geographical distribution of customers will affect the proportion of each part of the total cost. Enterprises can design different optimization objective functions according to different geographical distribution types of customers.
4. Experimental results of different regulatory factors show that the difference in time sensitivity of fresh agricultural products has a great impact on the total cost and distribution plan. With the reduction of time sensitivity of fresh agricultural products, the total cost will be greatly reduced, and the proportion of fresh loss cost to total cost will also be greatly reduced.
5. The experiments on the relationship between itemized costs show that carbon emission costs account for the lowest proportion of total costs. Assign weight to each itemized cost. If the weight of carbon emission cost increases, the cost of carbon emission will decrease while the total cost will increase. If the weight of distribution cost and freshness loss cost increases, the total cost decreases. That is, when companies focus on reducing carbon emissions, the total cost increases.
6. In this paper, standard calculation examples were used for the experiment. The standard calculation examples lack road slope data, which have an impact on the carbon emissions of vehicles. Future studies can be conducted on the basis of real cases with road slope, and more accurate conclusions on carbon emissions will be drawn.