Vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve the VRP and its variants. Although existing approaches have contributed a lot to the development of this field, these approaches either are limited in problem size or need manual intervening in choosing parameters. To solve these difficulties, many studies have considered the learning-based optimization (LBO) algorithms to solve the VRP. This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches. We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms. Finally, we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.
@article{arxiv.2107.07076,
title = {An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem},
author = {Bingjie Li and Guohua Wu and Yongming He and Mingfeng Fan and Witold Pedrycz},
journal= {arXiv preprint arXiv:2107.07076},
year = {2022}
}