用强化学习优化农业车辆路径，显著降低油耗和行程时间。
JPDS-NN: Reinforcement Learning-Based Dynamic Task Allocation for Agricultural Vehicle Routing Optimization
- 基于图注意力机制的神经网络，将路径规划建模为马尔可夫决策过程。
- 相比基线方法，路程减少48.4%-65.4%，计算速度提升百倍以上。
- 适合动态变化的不规则农田场景，适用于大规模智能农用车调度。
入口依赖型车辆路径问题（EDVRP）是车辆路径问题（VRP）的一种变体，其路由结果受城市规模影响，需考虑入口因素。本文聚焦农业场景下的EDVRP，针对不规则地块的多参数车辆规划问题。传统启发式方法常忽略地块几何形状与入口约束，为此提出联合概率分布采样神经网络（JPDS-NN），采用编码器-解码器结构结合图变换器与注意力机制，将路径规划建模为马尔可夫决策过程，并通过强化学习实现高效、快速的端到端规划。实验表明，JPDS-NN在旅行距离上减少48.4%-65.4%，燃料消耗降低14.0%-17.6%，计算速度比基线方法快两个数量级，且在动态调度场景中性能优于基线15%-25%。消融实验验证了交叉注意力与预训练的必要性。该框架支持在动态约束下大规模农场的可扩展智能路径规划。
原文摘要 · Abstract (English)
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.
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