系统仿真学报 ›› 2022, Vol. 34 ›› Issue (8): 1775-1788.doi: 10.16182/j.issn1004731x.joss.21-0244

• 仿真建模理论与方法 • 上一篇    下一篇

时间依赖型多时间窗车辆路径问题研究

李楠1(), 胡蓉1(), 钱斌1,2, 金怀平1, 于乃康2   

  1. 1.昆明理工大学 信息工程与自动化学院,云南 昆明 650500
    2.昆明理工大学 机电工程学院,云南 昆明 650500
  • 收稿日期:2021-03-23 修回日期:2021-06-06 出版日期:2022-08-30 发布日期:2022-08-15
  • 通讯作者: 胡蓉 E-mail:2235750519@qq.com;ronghu@vip.163.com
  • 作者简介:李楠(1995-),男,硕士生,研究方向为复杂系统智能优化。E-mail:2235750519@qq.com
  • 基金资助:
    国家自然科学基金(61963022)

Research on Time-dependent Vehicle Routing Problem with Multiple Time Windows

Nan Li1(), Rong Hu1(), Bin Qian1,2, Huaiping Jin1, Naikang Yu2   

  1. 1.School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
    2.School of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
  • Received:2021-03-23 Revised:2021-06-06 Online:2022-08-30 Published:2022-08-15
  • Contact: Rong Hu E-mail:2235750519@qq.com;ronghu@vip.163.com

摘要:

针对一类考虑城市交通拥堵情况的时间依赖型多时间窗车辆路径问题(time-dependent vehicle routing problem with multiple time windows,TD_VRPMTW),提出一种混合离散灰狼算法(hybrid discrete grey wolf optimizer,HDGWO)进行求解。在HDGWO中,设计了新的灰狼个体更新公式,采用基于客户排列的整数编码方式使算法可直接在离散问题解空间中执行基于标准灰狼算法个体更新机理的全局搜索设计了基于问题性质的种群初始化策略用于生成具有高质量和多样性的初始种群引入头狼信息交流公式用于探索头狼形成的优质解空间构造具有多种局部搜索操作的自适应变邻域局部搜索策略用于增强算法的局部搜索能力。结果表明:HDGWO可有效求解TD_VRPMTW。

关键词: 灰狼算法, 多时间窗, 车辆路径问题, 时间依赖, 离散, 局部搜索

Abstract:

Aiming at the time-dependent vehicle routing problem with multiple time windows (TD_VRPMTW) that considers urban traffic congestion, a hybrid discrete gray wolf optimizer (HDGWO) is proposed. In the HDGWO, a new grey wolf individual updating formula is designed, and the integer coding method based on customer permutation is adopted, so that the algorithm can directly perform the global search based on GWO individual updating mechanism in the discrete problem solution space.A population initialization strategy based on the nature of the problem is designed to generate the initial population with high quality and diversity.The information exchange formula of the head wolf is introduced to explore the high-quality solution space formed by the head wolf. An adaptive variable neighborhood local search strategy with multiple local search operators is constructed to enhance the local search ability of the algorithm. Results show that HDGWO can effectively solve TD_VRPMTW.

Key words: GWO, multiple time windows, vehicle routing problem, time-dependent, discrete, local search

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