计算技术与自动化2024，Vol.43Issue(4)：153-160,8.DOI:10.16339/j.cnki.jsjsyzdh.202404025
基于强化学习的带软时间窗多行程绿色车辆路径优化研究
Reinforcement Learning Based Path Optimization for Multi-Trip Green Vehicles with Soft Time Window
摘要
Abstract
In order to assist the logistics industry in achieving its goal of peak carbon dioxide emissions and carbon neu-trality,construction must be facilitated,and a green logistics industry must be rapidly established and developed.Firstly,a multi-trip green vehicle path optimization model with soft time window constraints is constructed by comprehensively consid-ering the factors of fuel consumption,carbon emission,manpower,vehicles,and user experience.Subsequently,the Pin-SAGE graph network,TRPO,and GAE methods are considered collectively to enhance the deep reinforcement learning opti-mization algorithm of Actor-Critic.Ultimately,the Actor-Critic algorithm is employed to address the model for the green multi-trip vehicle path scheme.Experimental evidence indicates that the solution method proposed in the paper is an effective means of planning green vehicle routes,which in turn has the potential to significantly reduce logistics costs and realise the dual optimisation of economic and environmental benefits for logistics enterprises.关键词
绿色物流/软时间窗/深度强化学习/Actor-Critic框架Key words
green logistics/soft time window/deep reinforcement learning/Actor-Critic framework分类
管理科学引用本文复制引用
姚利军,王可君..基于强化学习的带软时间窗多行程绿色车辆路径优化研究[J].计算技术与自动化,2024,43(4):153-160,8.基金项目
湖南省烟草公司长沙市公司科技项目(CS2023KJ09) （CS2023KJ09）
