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

作　　者：Bingjie Li Guohua Wu Yongming He Mingfeng Fan Witold Pedrycz 

机构地区：[1]School of Traffic and Transportation Engineering,Central South University,Changsha 410075,China [2]College of Systems Engineering,National University of Defense Technology,Changsha 410073,China [3]Department of Electrical and Computer Engineering,University of Alberta,Edmonton,AB T6G 2V4,Canada [4]the Department of Electrical and Computer Engineering,Faculty of Engineering,King Abdulaziz University,Jeddah 21589,Saudi Arabia [5]the Systems Research Institute,Polish Academy of Sciences,Warsaw 01447,Poland [6]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第7期1115-1138,共24页自动化学报（英文版）

摘　　要：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.

关 键 词：End-to-end approaches learning-based optimization(LBO)algorithms reinforcement learning step-by-step approaches vehicle routing problem(VRP) 

分 类 号：U492.22[交通运输工程—交通运输规划与管理] TP18[交通运输工程—道路与铁道工程]