Edge-based Formulation with Graph Attention Network for Practical Vehicle Routing Problem with Time Windows

Vehicle routing problem with time windows (VRPTW) is an important topic in modern delivery companies. Optimizing the vehicle routes not only reduces the transportation cost but also increases the customers' satisfaction. In literature, there are many studies focusing on symmetric vehicle routing problems. However, due to the transportation network and traffic conditions, the traveling distance and traveling time may be asymmetric in practical scenarios. In this paper, we formulate a practical VRPTW from the perspective of edges. With the edge-based formulation, a novel deep reinforcement learning model based on graph attention network is proposed. Two benchmark sets of practical VRPTW for training and testing are generated from the real-world data. The experimental results on the benchmark sets demonstrate that our method can outperform node-based and other well-known methods.