Deep Reinforcement Learning Optimizes Vehicle Routing for Logistics

The Vehicle Routing Problem (VRP) remains a significant challenge in the supply chain and transportation industry, despite advancements in digital platforms and algorithms. Traditional optimization models often struggle with the complexities of real-world scenarios, including numerous specific constraints, uncertain information, and unpredictable human behavior. This research investigates how deep reinforcement learning (DRL) can provide more effective solutions for VRP. The paper presents three industrial case studies focusing on external truck network design. In these scenarios, DRL-based optimization agents were implemented to plan routes. The results showed that the routes optimized by the reinforcement learning agent achieved over 10% total cost savings compared to baseline methods. This demonstrates DRL's capability to handle the intricate dynamics of industrial logistics, offering a promising avenue for future research to generalize DRL algorithms to even more variations of VRP.