考虑时变速度的多车场绿色车辆路径模型及优化算法研究
作者:
作者单位:

1.湖南工商大学 大数据与互联网创新研究院;2.湖南工商大学 湖南省移动电子商务协同创新中心;3.湖南工商大学 会计学院

作者简介:

通讯作者:

中图分类号:

F252;U116

基金项目:

国家自然科学基金面上项目(71972069);湖南省高校物流系统优化与运作管理科技创新团队


Research on multi-depot green vehicle routing model and its optimization algorithm with time-varying speed
Author:
Affiliation:

1.Research Institute of big data and Internet innovation,Hunan University of Technology and Business;2.Mobile E-business Collaborative Innovation Center of Hunan Province,Hunan University of Technology and Business;3.School of accounting,Hunan University of Technology and Business

Fund Project:

The National Natural Science Foundation of China (General Program,71972069);Hunan University logistics system optimization and operation management science and technology innovation team

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    摘要:

    针对多车场绿色车辆路径问题,考虑顾客的坐标位置,采用K-means聚类方法将顾客分配给不同的车场;分析时变速度和实时载重对车辆油耗和碳排放的影响,确定车辆油耗和碳排放的度量函数;在此基础上,构建以油耗成本、碳排放成本、车辆使用成本、驾驶员工资以及时间窗惩罚成本之和最小作为优化目标的多车场绿色车辆路径规划模型,并根据模型特点设计一种改进蚁群算法求解。算例仿真结果表明,构建的模型和提出的算法能合理调度车辆,有效规避交通拥堵时间段,降低配送总成本,减少车辆油耗和碳排放,促进物流配送企业的节能减排。

    Abstract:

    Aiming at the multi-depot green vehicle routing problem,the coordinate position of customers are considered,k-means clustering method is used to assign customers to different depots,the influence of time-varying speed and real-time load on vehicle fuel consumption and carbon emission is analyzed,and the measurement function of fuel consumption and carbon emission is determined. On this basis,a multi-depot green vehicle routing model is established with the optimization objective of minimizing the sum of fuel consumption cost,carbon emission cost,use cost of vehicles,drivers’wages and time window penalty cost. According to the characteristics of the model,an improved ant colony algorithm is designed.The experimental results show that the model and algorithm can reasonably schedule vehicles,effectively avoid periods of traffic congestion and reduce the total distribution costs,reduce vehicle fuel consumption and carbon emissions,and promote energy saving and emission reduction of logistics and distribution enterprises.

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历史
  • 收稿日期:2020-10-16
  • 最后修改日期:2020-12-02
  • 录用日期:2020-12-03
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