An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem

The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem, and many models algorithms have been proposed to solve the VRP its variants. Although existing approaches contributed significantly development of this field, these either are limited in size or need manual intervention choosing parameters. To difficulties, studies considered learning-based (LBO) VRP. This paper reviews recent advances field divides relevant into end-to-end step-by-step approaches. We performed statistical analysis reviewed articles from various aspects designed three experiments evaluate performance four representative LBO algorithms. Finally, we conclude applicable types problems for different suggest directions which researchers can improve