The Entrance Dependent Vehicle Routing Problem (EDVRP) is a variant of the Vehicle Routing Problem (VRP) where the scale of cities influences routing outcomes, necessitating consideration of their entrances. This paper addresses EDVRP in agriculture, focusing on multi-parameter vehicle planning for irregularly shaped fields. To address the limitations of traditional methods, such as heuristic approaches, which often overlook field geometry and entrance constraints, we propose a Joint Probability Distribution Sampling Neural Network (JPDS-NN) to effectively solve the EDVRP. The network uses an encoder-decoder architecture with graph transformers and attention mechanisms to model routing as a Markov Decision Process, and is trained via reinforcement learning for efficient and rapid end-to-end planning. Experimental results indicate that JPDS-NN reduces travel distances by 48.4-65.4%, lowers fuel consumption by 14.0-17.6%, and computes two orders of magnitude faster than baseline methods, while demonstrating 15-25% superior performance in dynamic arrangement scenarios. Ablation studies validate the necessity of cross-attention and pre-training. The framework enables scalable, intelligent routing for large-scale farming under dynamic constraints.

翻译：入口依赖型车辆路径问题（EDVRP）是车辆路径问题（VRP）的一个变体，其中城市规模影响路径规划结果，因此必须考虑其入口。本文针对农业领域的EDVRP展开研究，重点关注不规则形状田地的多参数车辆规划。为克服传统方法（如启发式算法）常忽略田地几何形状和入口约束的局限性，我们提出了一种联合概率分布采样神经网络（JPDS-NN）来有效求解EDVRP。该网络采用编码器-解码器架构，结合图Transformer和注意力机制，将路径规划建模为马尔可夫决策过程，并通过强化学习进行训练，实现高效快速的端到端规划。实验结果表明，JPDS-NN将行驶距离减少了48.4-65.4%，降低了14.0-17.6%的燃油消耗，计算速度比基准方法快两个数量级，同时在动态调度场景中表现出15-25%的优越性能。消融研究验证了交叉注意力与预训练的必要性。该框架能够为动态约束下的大规模农业作业提供可扩展的智能路径规划方案。