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机械工程学报

机械工程学报 ›› 2024, Vol. 60 ›› Issue (4): 458-472.doi: 10.3901/JME.2024.04.458

• 交叉与前沿 • 上一篇    

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考虑排队因素的多车型车辆配置与路径协同优化

唐红涛1,2, 魏书鹏1,2, 李西兴3,4, 雷德明5, 汪开普1,2   

  1. 1. 武汉理工大学机电工程学院 武汉 430070;
    2. 机器人与智能制造湖北省工程研究中心 武汉 430070;
    3. 湖北工业大学机械工程学院 武汉 430068;
    4. 湖北工业大学现代制造质量工程湖北省重点实验室 武汉 430068;
    5. 武汉理工大学自动化学院 武汉 430070
  • 收稿日期:2023-04-17 修回日期:2023-10-31 出版日期:2024-02-20 发布日期:2024-05-25
  • 通讯作者: 李西兴,男,1990年出生,副教授,硕士研究生导师。主要研究方向为生产调度与优化、智能算法设计。E-mail:li_xi_xing@126.com
  • 作者简介:唐红涛,男,1987年出生,副教授,博士研究生导师。主要研究方向为智能优化算法及制造企业信息化应用。E-mail:tanghongtaozc@163.com;魏书鹏,男,1999年出生。主要研究方向为调度理论与智能优化。E-mail:weishupeng@whut.edu.cn;雷德明,男,1968年出生,教授,博士研究生导师。主要研究方向为智能优化与调度。E-mail:deminglei11@163.com;汪开普,男,1991年出生,博士,副研究员。主要研究方向为可持续制造、智能制造与智能优化。E-mail:wangkaipu@whut.edu.cn
  • 基金资助:
    国家自然科学基金(51805152,52075401); 湖北省科技厅自然科学基金面上(2022CFB445); 湖北工业大学高层次人才科研基金(GCRC2020009)资助项目

Coordinated Optimization of Heterogeneous Vehicle Allocation and Route Considering Queuing Factors

TANG Hongtao1,2, WEI Shupeng1,2, LI Xixing3,4, LEI Deming5, WANG Kaipu1,2   

  1. 1. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070;
    2. Hubei Provincial Engineering Research Center of Robotics and Intelligent Manufacturing, Wuhan 430070;
    3. School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068;
    4. Hubei Key Laboratory of Modern Manufacturing and Quality Engineering, Hubei University of Technology, Wuhan 430068;
    5. School of Automation, Wuhan University of Technology, Wuhan 430070
  • Received:2023-04-17 Revised:2023-10-31 Online:2024-02-20 Published:2024-05-25

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摘要： 针对制造企业内车辆物流水平低、效率低、成本高等问题,考虑多车型、多品种货物以及车辆排队等待、车辆数量受限等因素,建立以总物流完成时间、总行驶里程、总物流成本为目标的车辆配置与路径协同优化模型。针对问题特征,提出一种混合离散麻雀搜索算法进行求解。在算法中,设计基于Tent混沌序列的反向学习初始化策略,以提高种群的多样性;针对不同麻雀种群,设计离散化策略以适应问题的离散特征;结合车型与路径特征,构造多种局部搜索策略,以提高算法的局部搜索能力;引入模拟退火Metropolis准则,并设计多目标解的保留策略,以避免算法陷入局部最优。通过对某水泥企业的实际车辆物流案例分析,验证所提策略的有效性以及所提算法的优越性。所得物流方案可以有效降低11%的物流成本,提高19.8%的物流效率,并减少29.1%的车辆行驶里程。

关键词: 多车型车辆路径, 车辆配置, 混合离散麻雀搜索算法, 多目标优化, 车辆排队

Abstract: Aiming at the problems of low level, low efficiency and high cost of the vehicle logistics in manufacturing enterprises, a mathematical model for collaborative optimization vehicle allocation and route is established. The model considers a variety of factors,including heterogeneous vehicles, cargo types, vehicle queuing and vehicle quantity. In addition, the logistics completion time,vehicle mileage and logistics cost are employed as the objectives of the model. To solve the model effectively, a hybrid discrete sparrow search algorithm is proposed. In the algorithm, an initialization method based on reverse learning is used to improve the diversity of the population, several discretization strategies are designed to accommodate the discrete characteristics of the problem.Moreover, a variety of local search strategies are designed to improve the search ability, and a solution retention strategy is designed to avoid the algorithm falling into local optimization. By analyzing an actual case of a cement enterprise, the effectiveness of the proposed strategies and the superiority of the proposed algorithm are verified. The obtained logistics scheme can effectively reduce the logistics cost by 11%, improve the logistics efficiency by 19.8%, and reduce the vehicle mileage by 29.1%.

Key words: heterogeneous vehicle routing, vehicle allocation, hybrid discrete sparrow search algorithm, multi-objective optimization, vehicle queuing

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