Dear Editor, This letter presents a multi-automated guided vehicles (AGV) routing planning method based on deep reinforcement learning (DRL) and recurrent neural network (RNN), specifically utilizing proximal policy optimization (PPO) and long short-term memory (LSTM). Compared to traditional AGV pathing planning methods using genetic algorithm, ant colony optimization algorithm, etc., our proposed method has a higher degree of adaptability to deal with temporary changes of tasks or sudden failures of AGVs. Furthermore, our novel routing method, which uses LSTM to take into account temporal step information, provides a more optimized performance in terms of rewards and convergence speed as compared to existing PPO-based routing methods for AGVs.

Proper planning of charging infrastructure can significantly facilitate the popularization of electric vehicles and alleviate users’ mileage anxiety. Charging station siting and sizing are two key challenges in the planning with each of them being a complex optimization problem. In this paper, a multi-objective optimization approach is proposed to solve them together. First, considering that accurate charging demand estimation is crucial for planning, a traffic road network is established for this purpose. A Monte Carlo method is used to estimate the spatiotemporal distribution of charging demand in a region based on the probabilistic characteristics of user trips. Since uncoordinated charging not only increases the load but also leads to unstable operation of the local power system, a heuristic algorithm is proposed to coordinate charging scheduling. Then, based on the scheduled demand, this paper proposes a framework for the siting and sizing of charging stations to optimize the benefits for both operators and users by minimizing the construction, operation and maintenance costs, and the user’s detour time. As the given problem is a complex multi-objective combinatorial optimization problem, it is easy to fall into local optimum if traditional evolutionary algorithms are employed. Therefore, a multi-objective dynamic binary particle swarm optimization method is designed to solve this problem effectively. Finally, experimental simulations show that the proposed method outperforms the other comparative algorithms in terms of solution quality. A case study is presented to demonstrate the applicability and effectiveness of the proposed method in optimizing the location and capacity of charging stations.