Dynamic Optimization of Vehicle Production Planning in Transportation Networks Using Federated Reinforcement Learning

Modern transportation networks, with their complexity and dynamic nature, have a substantial demand for intelligent vehicles. Developing effective production strategies for smart vehicles is essential to reducing both production costs and energy consumption. Traditional vehicle production planning has largely depended on heuristic algorithms and solvers, which lack scalability and are susceptible to local optima. Furthermore, existing solutions do not concurrently address both dynamic and regular vehicle production planning. To overcome these limitations, this paper proposes an effective optimizing method for large-scale smart manufacturing within intelligent transportation networks using Federated Reinforcement Learning. In our proposal, the Gated Recurrent Unit and Asynchronous Advantage Actor Critic (A3C) reinforcement algorithms are employed to develop a Dynamic Optimizing Planning Module(DOPM), which can output an excellent solution of 1000 vehicles within 5 seconds. A High-Quality Processing Module(HQPM) is constructed by the Transformer with A3C, significantly enhancing the production plan's quality. Finally, the proposed methods will integrate with Federated Learning (FL) to establish a scalable, privacy-preserving intelligent manufacturing scheduling framework for transportation networks. Experimental results demonstrate that our work significantly outperforms traditional solutions, achieving over a 93% improvement in solving speed and reducing constraint violations by more than 95%.