摘要
为了助力物流行业响应“碳达峰”和“碳中和”建设目标,提速绿色物流产业的建立与发展,首先综合考虑油耗、碳排放、人力、车辆、用户体验等因素,构建带软时间窗约束的多行程绿色车辆路径优化模型。然后综合考虑PinSAGE图网络、TRPO和GAE方法来改进Actor-Critic的深度强化学习优化算法,最后采用Actor-Critic算法对模型对绿色多行程车辆路径方案求解。实验表明,提出的求解方法能高效规划绿色车辆路径,进而显著减少物流成本,实现物流企业经济效益与环境效益的双重优化。
In order to assist the logistics industry in achieving its goal of peak carbon dioxide emissions and carbon neutrality,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 considering the factors of fuel consumption,carbon emission,manpower,vehicles,and user experience.Subsequently,the PinSAGE graph network,TRPO,and GAE methods are considered collectively to enhance the deep reinforcement learning optimization 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.
作者
姚利军
王可君
YAO Lijun;WANG Kejun(Changzhutan Tobacco Logistics Co.,Ltd.of Hunan,Changsha,Hunan 410004,China)
出处
《计算技术与自动化》
2024年第4期153-160,共8页
Computing Technology and Automation
基金
湖南省烟草公司长沙市公司科技项目(CS2023KJ09)。
