## 5 Experiments
### Discussion of the PGDD and Interpretation of the Results  
From the above discussion, the conclusion can be reached that the proposed PGDD framework introduces significant theoretical and algorithmic advancements in the planning of the route of unmanned delivery vehicles. This study has presented a novel point-graph joint embedding and dual-decoder mechanism that co-optimizes customer satisfaction and cost efficiency, addressing limitations of traditional approaches such as VRPTW, which focus primarily on single-objective optimization.  
This research extends existing work by integrating fuzzy time-window penalties and pseudo-label cross-supervision to mitigate the limitations of static time-window methods.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Seed = 1234** & **Seed = 1236** & **Epoch Time (s)** \\ \hline N = 1 & 47.66 & 47.15 & 478 \\ N = 2 & 47.10 & 47.33 & 496 \\ N = 3 & **46.82** & **46.74** & **565** \\ N = 4 & 47.13 & 47.03 & 585 \\ N = 5 & 48.47 & 47.43 & 594 \\ \hline \end{tabular} The bold numbers represent the values corresponding to the best hyperparameter collocation.  
\end{table}
Table 13: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Seed = 1234** & **Seed = 1236** & **Seed = 1238** \\ \hline
**Satisfaction** & **46.82** & **46.74** & **48.68** \\ No Satisfaction & 55.43 & 56.77 & 50.22 \\ \hline
**tanh\_function** & **46.82** & **46.74** & **48.68** \\ sigmoid\_function & 48.25 & 49.13 & 49.32 \\ gaussian\_function & 54.51 & 53.68 & 55.38 \\ linear\_function & 51.94 & 50.92 & 51.92 \\ \hline \end{tabular} The bold numbers refer to the best result.  
\end{table}
Table 14: Effect of whether to consider customer satisfaction.  
These contributions provide a robust foundation for multi-objective decision-making in large-scale logistics operations. The results align well with previous studies showing that hybrid learning-based approaches improve route adaptability, yet PGDD further enhances real-time optimization through dynamic reweighting mechanisms. The experimental findings indicate that PGDD significantly outperforms the baseline models. Reduce total costs by 8.73% while improving customer satisfaction by 15.2% in 100-node tasks. Moreover, PGDD demonstrates superior real-time capabilities, achieving sub-90 ms latency in large-scale logistics applications, and exhibits robustness by maintaining only a 2.3% path deviation under sensor noise, surpassing conventional methods. These results reinforce the efficiency and adaptability of PGDD in dynamic delivery environments.  
Despite its effectiveness, PGDD has certain limitations. One limitation of this method is its high computational resource demand, requiring 24-h training on an A100 GPU for 400-node scenarios, which may hinder adoption by small and medium enterprises (SMEs). Furthermore, PGDD currently relies on simulated obstacle scenarios, and its real-world performance remains to be validated with live sensor data from LiDAR and cameras. These aspects require further investigation to improve the practicality of the model in real-time logistics systems.  
Notwithstanding these limitations, this study suggests significant implications for the logistics industry. An important future direction of this research is the deeper integration of PGDD with real-time sensory data to enhance obstacle avoidance and dynamic route adjustments. Furthermore, future iterations of PGDD may incorporate cloud-edge collaborative optimization frameworks to address its computational intensity, enabling real-time decision-making for large-scale logistics networks. The results of this study will hopefully serve as useful feedback for further improvements in intelligent routing systems, contributing to more efficient and adaptive unmanned delivery vehicle operations.