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Volume 9 Issue 7
Jul.  2022

IEEE/CAA Journal of Automatica Sinica

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B. J. Li, G. H. Wu, Y. M. He, M. F. Fan, and W. Pedrycz, “An overview and experimental study of learning-based optimization algorithms for the vehicle routing problem,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1115–1138, Jul. 2022. doi: 10.1109/JAS.2022.105677
Citation: B. J. Li, G. H. Wu, Y. M. He, M. F. Fan, and W. Pedrycz, “An overview and experimental study of learning-based optimization algorithms for the vehicle routing problem,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1115–1138, Jul. 2022. doi: 10.1109/JAS.2022.105677

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

doi: 10.1109/JAS.2022.105677
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  • 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.


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    • The learning-based optimization (LBO) algorithms have been applied to vehicle routing problems (VRP), and many remarkable research breakthroughs have been achieved. The paper briefly introduces the applications of LBO algorithms to the VRP to aid beginners in understanding the development of this field
    • Although the LBO algorithms can overcome the deficiencies of the tedious parameter tuning of exact and heuristic algorithms and rapidly solve online instances with the advantage of offline training, the LBO algorithms still have some technological bottlenecks to be settled. The paper discusses the advantages and disadvantages of different learning-based optimization algorithms based on extensive experiments on different datasets
    • Using the LBO algorithms to solve the VRP is still under research and there are several challenges in the LBO algorithms that need to be settled in the future. The paper suggests several potential research directions of applying the LBO algorithms in the VRP from these limitations


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