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

TL;DR

This paper reviews recent learning-based optimization algorithms for the vehicle routing problem, analyzing their performance, limitations, and potential improvements through experiments and categorization.

Contribution

It provides a comprehensive review, statistical analysis, and experimental evaluation of LBO algorithms for VRP, highlighting their applicability and future research directions.

Findings

LBO algorithms can effectively solve VRP with certain problem sizes.

End-to-end approaches outperform step-by-step methods in specific scenarios.

Recommendations for improving LBO algorithms are proposed.

Abstract

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.