JPDS-NN: Reinforcement Learning-Based Dynamic Task Allocation for Agricultural Vehicle Routing Optimization

• URL: http://arxiv.org/abs/2503.02369v1
• Date: Tue, 04 Mar 2025 07:50:32 GMT
• Title: JPDS-NN: Reinforcement Learning-Based Dynamic Task Allocation for Agricultural Vehicle Routing Optimization
• Authors: Yixuan Fan, Haotian Xu, Mengqiao Liu, Qing Zhuo, Tao Zhang,
• Abstract summary: The Entrance Dependent Vehicle Routing Problem (EDVRP) is a variant of the Vehicle Problem (VRP) where the scale of cities influences routing outcomes.<n>We propose a Joint Probability Distribution Sampling Neural Network (JPDS-NN) to effectively solve the EDVRP.<n>We show 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.
• Score: 3.559425487157277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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