Reinforcement Learning Approaches to Dynamic Routing and Distribution Network Design

Abstract

Dynamic routing and distribution network design represent critical challenges in modern logistics and transportation systems, where decisions must adapt to rapidly changing environmental conditions and operational constraints. Reinforcement learning (RL) has emerged as a powerful paradigm for addressing these challenges by enabling autonomous agents to learn optimal policies through interaction with complex, uncertain environments. This review examines recent advances in RL applications to dynamic routing problems and distribution network design, focusing on methodological innovations and practical implementations. The paper explores fundamental RL algorithms including deep Q-networks (DQN), policy gradient methods, and actor-critic architectures, analyzing their suitability for different routing scenarios. We investigate how RL approaches handle real-time traffic dynamics, demand uncertainty, and multi-objective optimization in distribution systems. The review synthesizes findings from recent literature on hybrid methods combining RL with traditional optimization techniques, multi-agent RL (MARL) for coordinated routing decisions, and transfer learning strategies for network adaptation. Key applications examined include vehicle routing problems (VRP), last-mile delivery optimization, urban traffic management, and supply chain network configuration. This comprehensive analysis reveals that RL methods demonstrate superior performance in handling dynamic uncertainties compared to conventional approaches, though challenges remain in scalability, sample efficiency, and real-world deployment. The paper concludes by identifying promising research directions including federated RL for privacy-preserving logistics optimization, graph neural network (GNN) integration for spatial reasoning, and explainable RL frameworks for decision transparency.