부산대학교 도서관

This paper focuses on a deep reinforcement learning (DRL)-based approach for Heterogeneous Vehicle Routing Problem, where each vehicle in the fleet is characterized by its capacity and speed. Previous methods fail to optimize the route for a vehicle while cooperatively considering the current state (e.g., the remaining capacities and the positions) of other vehicles in the fleet. To solve this problem, we first propose a chronological Markov Decision Process, which makes the current state of the whole fleet available. Second, we propose a fleet encoder, a specific network architecture to promote cooperative route generation among vehicles by incorporating the fleet state. Experimental results show that our method outperforms other DRL-based methods on problems with any number of customers and vehicles.