知识驱动下考虑司机经验的车辆路径问题研究
CSTR:
作者:
作者单位:

河海大学 商学院,南京 211100

作者简介:

通讯作者:

E-mail: wei.xiao@hhu.edu.cn.

中图分类号:

C934

基金项目:

国家自然科学基金面上项目(62106098,42071278,62272210);广东省类脑智能计算重点实验室项目(2020B121201001);深圳市自然科学基金稳定支持计划项目(20200925154942002).


Research on vehicle routing problem with driver experience under knowledge-driven approach
Author:
Affiliation:

School of Business,Hohai University,Nanjing 211100,China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    在实际物流运输中,通过对大量历史路径数据进行挖掘可以获得司机在线路选择时的行为偏好,并辅助司机规避各类潜在风险,提高路径规划的可靠性.基于此,考虑司机经验的车辆路径问题,设计一个综合考虑路径可靠度和行驶距离的双重路径评价指标,建立对应的整数规划模型,在充分分析问题特征的基础上提出一种知识驱动型动态多起点变邻域搜索算法.首先,利用广义序列模式挖掘从历史配送路径集中提取出频繁序列和潜力序列两类经验路径;然后,融合上述经验路径提出一种基于知识的冲突消解策略来构建高质量初始解;最后,使用动态多起点变邻域搜索对初始解进行改进.结合某珠宝公司实际物流配送数据发现,与传统的变邻域搜索算法相比,所提出算法可极大地降低问题的规模和求解时间,在有效缩短行驶距离的同时提高路径规划的可靠度,为实际物流企业的路径规划提供决策依据.

    Abstract:

    In real-world logistics transportation, leveraging historical route data can provide valuable insights into drivers' route preferences, enabling them to avoid potential risks and enhance route planning reliability. Based on this, this paper studies the vehicle routing problem with the driver's experience, introduces a dual path evaluation index considering both the path reliability and driving distance, and then establishes the corresponding integer programming model. On the basis of fully analyzing the characteristics of the problem, a knowledge-based dynamic multi-start variable neighborhood search algorithm is proposed. Firstly, generalized sequence pattern mining techniques are employed to extract experience paths, including frequent and potential sequence, from a large dataset of vehicle trajectories. Then, a knowledge-based conflict resolution strategy is proposed to construct high-quality initial solutions by integrating the aforementioned experience paths. Finally, a dynamic multi-start variable neighborhood search algorithm is introduced to improve the initial solutions. Through empirical analysis using real logistics distribution data from a jewelry company, the proposed algorithm demonstrates significant improvements compared to traditional variable neighborhood search algorithms. It effectively reduces the scale and solving time of the problem, while simultaneously minimizing driving distance and improving the reliability of path planning, which provide a valuable decision-making foundation for path planning in actual logistics enterprises.

    参考文献
    相似文献
    引证文献
引用本文

许瑞,朱燕燕,肖巍.知识驱动下考虑司机经验的车辆路径问题研究[J].控制与决策,2024,39(11):3848-3858

复制
相关视频

分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2024-09-20
  • 出版日期: 2024-11-20
文章二维码