We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using
reinforcement learning. In this approach, we train a single policy model that finds near …

Our model is based on the graph convolutional network (GCN) with node feature
(coordination and demand) and edge feature (the real distance between nodes) as input …

Traveling salesman and vehicle routing problems with their variants, as classic
combinatorial optimization problems, have attracted considerable attention for decades of …

Abstract Traveling Salesman Problem (TSP), as a classic routing optimization problem
originally arising in the domain of transportation and logistics, has become a critical task in …

With the emergence of crowdshipping and sharing economy, vehicle routing problem with
occasional drivers (VRPOD) has been recently proposed to involve occasional drivers with …

In the realization of smart cities, the most important component is the smart logistics in which
the vehicle routing problem (VRP) plays a significant role. The VRP has been proven to be …

The growth in online goods delivery is causing a dramatic surge in urban vehicle traffic from
last-mile deliveries. On the other hand, ride-sharing has been on the rise with the success of …

Routing delivery vehicles to serve customers in dynamic and uncertain environments like
dense city centers is a challenging task that requires robustness and flexibility. Most existing …

Many real-world vehicle routing problems involve rich sets of constraints with respect to the
capacities of the vehicles, time windows for customers etc. While in recent years first …

Order dispatching and driver repositioning (also known as fleet management) in the face of
spatially and temporally varying supply and demand are central to a ride-sharing platform …