## 2 Related Work
### Attention Mechanism in Route Planning Problem  
Some studies use some improved models in more complex scenarios, such as introducing an attention mechanism. [PERSON] et al. (2018) successfully implemented a learning algorithm for routing problems, introducing pointer attention networks (PN) for outputting permutations of inputs. Its inspiration derives from neural network-driven machine translation models ([PERSON] et al., 2018). The Traveling Salesman Problem (TSP) is a specific instance of the Vehicle Routing Problem (VRP) that is relatively easy to solve computationally, hence usually serving as the starting point for solving other routing problems. [PERSON] et al. (2018) expanded this work, introducing the Actor & Critic algorithm ([PERSON] et al., 2018) for training unsupervised solutions of PN networks. They treat each instance as a training sample and employ a policy gradient estimate of cost-unbiased Monte Carlo problem sampling solutions. [PERSON] et al. (2018) employed reinforcement learning (RL) to train graph pointer networks (GPN) to solve the Traveling Salesman Problem (TSP), where GPN is built based on the pointer network, capturing relationships between nodes by Implementing a graph embedding layer as the input. [PERSON] et al. (2018) introduced multihead attention mechanisms to solve various path optimization problems, significantly improving compared to pointer networks. [PERSON] et al. (2018) introduced an enhanced multihead attention and attention-based attention transformer model to address the issue of low-carbon multi-site vehicle routing. However, these algorithms only use reinforcement learning for training samples, lack reliable label support, and may overfit the training data during the training process, resulting in insufficient generalization ability in the face of new and unseen environments and cannot effectively solve the path planning problem. Existing studies have also employed unmanned delivery vehicles in diverse real-life situations. Unmanned delivery vehicles are becoming more and more important in the logistics business, and different algorithms are used to improve their routing efficiency. [PERSON] et al. (2018) introduced a dynamic-window algorithm employing dynamic priority for the path-planning of multi-AGV systems within dynamic environments. [PERSON] et al. (2018) investigated the charging dispatching problem for electric unmanned vehicles when charging stations have capacity constraints. [PERSON] et al. (2018) utilized a hybrid particle swarm optimization approach that combined particle swarm optimization and genetic algorithms to optimize paths. This technique attains superior, optimal solutions with a reduced number of iterations and enhanced stability. However, when it comes to designing the path for unmanned delivery vehicles, the majority of academics have overlooked the constraints associated with customer satisfaction or the expenses of compensating for deliveries that exceed the boundaries of customer tolerance. In addition, current methods fail to tackle the difficulty of efficiently handling problems that arise from a growing number of delivery nodes. This study discusses the practical uses of autonomous delivery vehicles in logistics. It proposes a path-planning problem that includes charging and time management strategies for the vehicles and aims to enhance customer satisfaction. Moreover, a unique deep neural network model incorporating attention mechanisms is presented to tackle the difficulties arising from the vast number of nodes and intricate scenarios. Table 1, above, summarizes key methods for route planning and highlights their limitations. Although each approach brings innovative solutions--from heuristic algorithms and DRL-based methods to attention mechanisms--they often fall short when scaling to real-world, large-scale logistics applications. Many methods are limited to small datasets, are computationally intensive, or do not account for critical factors such as customer satisfaction.