A Deep Reinforcement-Learning-Based Route Optimization Model for Multi-Compartment Cold Chain Distribution

Jingming Hu and Chong Wang ()
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Jingming Hu: School of Management, Sichuan Agricultural University, Chengdu 611130, China
Chong Wang: School of Business and Tourism, Sichuan Agricultural University, Chengdu 611130, China

Mathematics, 2025, vol. 13, issue 13, 1-17

Abstract: Cold chain logistics is crucial in ensuring food quality and safety in modern supply chains. The required temperature control systems increase operational costs and environmental impacts compared to conventional logistics. To reduce these costs while maintaining service quality in real-world distribution scenarios, efficient route planning is essential, particularly when products with different temperature requirements need to be delivered together using multi-compartment refrigerated vehicles. This substantially increases the complexity of the routing process. We propose a novel deep reinforcement learning approach that incorporates a vehicle state encoder for capturing fleet characteristics and a dynamic vehicle state update mechanism for enabling real-time vehicle state updates during route planning. Extensive experiments on a real-world road network show that our proposed method significantly outperforms four representative methods. Compared to a recent ant colony optimization algorithm, it achieves up to a 6.32% reduction in costs while being up to 1637 times faster in computation.

Keywords: multi-compartment cold chain distribution; low carbon; heterogeneous vehicle routing problem; deep reinforcement learning; vehicle state encoding; dynamic vehicle state update mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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