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 and algorithms have been proposed to solve the VRP and its variants. Although existing approaches have contributed significantly to the development of this field, these approaches either are l...

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Bibliographic Details
Published in:IEEE/CAA journal of automatica sinica 2022-07, Vol.9 (7), p.1115-1138
Main Authors: Li, Bingjie, Wu, Guohua, He, Yongming, Fan, Mingfeng, Pedrycz, Witold
Format: Article
Language:English
Subjects:
Algorithms
Combinatorial analysis
End-to-end approaches
learning-based optimization (LBO) algorithms
Machine learning
Manuals
Optimization
reinforcement learning
Route planning
Routing
Statistical analysis
step-by-step approaches
Vehicle routing
vehicle routing problem (VRP)
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Summary:The 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 significantly to the development of this field, these approaches either are limited in problem size or need manual intervention in choosing parameters. To solve these difficulties, many studies have considered 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.
ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2022.105677