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

• Published in: IEEE/CAA journal of automatica sinica Vol. 9; no. 7; pp. 1115 - 1138
• Main Authors: Li, Bingjie, Wu, Guohua, He, Yongming, Fan, Mingfeng, Pedrycz, Witold
• Format: Journal Article
• Language: English
• Published: Piscataway Chinese Association of Automation (CAA) 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); School of Traffic and Transportation Engineering,Central South University,Changsha 410075,China%College of Systems Engineering,National University of Defense Technology,Changsha 410073,China%Department of Electrical and Computer Engineering,University of Alberta,Edmonton,AB T6G 2V4,Canada; Saudi Arabia,Systems Research Institute,Polish Academy of Sciences,Warsaw 01447,Poland; Department of Electrical and Computer Engineering,Faculty of Engineering,King Abdulaziz University,Jeddah 21589
• 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); step-by-step approaches; learning-based optimization (LBO) algorithms; End-to-end approaches; reinforcement learning; vehicle routing problem (VRP)
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2022.105677

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.