## 1 Introduction
Examples include the traveling salesman problem (TSP) and the capacitated vehicle routing problem (CVRP), neglecting the intricate realities of actual delivery scenarios. This requires a model that can train large-scale, complex path-planning problems. As described, Traditional methods for unmanned delivery vehicle route planning face two major challenges. To begin with, heuristic algorithms are inefficient at handling large-scale nodes under dynamic time-window constraints. For another, existing deep learning models that rely on a single decoder tend to fall into local optima and neglect the balance between customer satisfaction and cost. To address these issues, this study proposes a novel approach termed the Point-Graph Joint Embedding and Dual-Decoder (PGDD) method, which introduces the following innovations. First, the point-graph joint embedding integrates node features with the topological structure for the first time and employs a multihead attention mechanism to generate a joint embedding that significantly enhances the model's ability to capture the spatiotemporal relationships in complex distribution networks. Second, the dual-decoder collaborative optimization mechanism introduces a pseudo-label generation decoder driven by reinforcement learning and a sequential outcome decoder driven by supervised learning; the former explores globally optimal routes, while the latter constrains overfitting via pseudo-labels, thus achieving a balanced optimization of distribution cost and customer satisfaction. Third, a dynamic penalty mechanism, based on a fuzzy time-window customer satisfaction function, dynamically adjusts route weights, enabling PGDD to replan routes under overtime risk and improve customer satisfaction by 15.2% compared to traditional fixed penalty strategies. The experimental results demonstrated an 8.73% enhancement at most when dealing with 100 nodes in the performance of this strategy compared to alternative learning methods. Furthermore, the degree of enhancement escalates as the issue expands, indicating the efficacy of our approach in tackling significant obstacles. To summarize, the main contributions of this paper are as follows:  
1. We design a fuzzy time window model based on customer satisfaction for the distribution path-planning problem of UDVs with fuzzy time windows in urban peripheral areas, which includes a new method to calculate the customer satisfaction function and penalty function. This method improves the practicality of cost calculation, enhances the versatility of the method, and enhances its practical application. 2. We propose an enhanced deep reinforcement-learning algorithm model based on attention mechanisms combining point-graph joint embedding and a pseudo-label learning strategy to solve the real-life unmanned vehicle distribution problem. Concurrently, the model uses an end-to-end method to calculate the delivery path. 3. Extensive experiments indicate that the suggested approach outperforms competing algorithms with regard to accuracy, standard deviation, and other indicators. At the same time, the model allows training on data sets of various sizes, can solve problemsof different sizes, and has better output results. This makes our method very valuable for practical applications. The remainder of this work is structured as follows. Section 2 encompasses related research. Section 3 provides a detailed explanation of the problem to be solved. Section 4 provides a detailed explanation of the proposed method. Section 5 shows the experimental results of the proposed method, containing comparison experiments, ablation experiments, experiments on the influence of certain parameters, and qualitative analysis. Section 6 contains the discussion and conclusion of our work.