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Vehicle routing problem (VRP) is a typical discrete combinatorialoptimization problem, and many models and algorithms have been proposed tosolve the VRP and its variants. Although existing approaches have contributed alot to the development of this field, these approaches either are limited inproblem size or need manual intervening in choosing parameters. To solve thesedifficulties, many studies have considered the learning-based optimization(LBO) algorithms to solve the VRP. This paper reviews recent advances in thisfield and divides relevant approaches into end-to-end approaches andstep-by-step approaches. We performed a statistical analysis of the reviewedarticles from various aspects and designed three experiments to evaluate theperformance of four representative LBO algorithms. Finally, we conclude theapplicable types of problems for different LBO algorithms and suggestdirections in which researchers can improve LBO algorithms.

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