Task Planning for The Multi-center Heterogeneous Vehicle Pickup and Delivery Problem

With the development of e-commerce, using heterogeneous vehicles to transfer packages between multiple logistic centers has become more and more common. Accordingly, the multi-center heterogeneous vehicle pickup and delivery (MHVPD) problem has become a hot research topic. In the MHVPD problem, solving the collaborative relationship between heterogeneous vehicles is a very challenging problem. In this paper, we study the task planning among multiple logistic centers in response to the above challenge. We first formulate a mixed-integer linear programming model to address this problem. Then, we proposed a metaheuristic approach based on the integration of bipartite matching and adaptive large neighborhood search (ALNS). We demonstrate the efficiency of our algorithm by conducting experiments using real-world datasets, and reduce the average task completion time by up to 34.51%.