Research on Takeaway Delivery Path Planning Based on Improved ant Colony Algorithm
Affiliation:

1.Northeastern University;2.Zhejiang Sci-Tech University,Shanghai Dianji University

  • Article
  • | |
  • Metrics
  • |
  • Reference [22]
  • | |
  • Cited by
  • | |
  • Comments
    Abstract:

    In order to solve the problem of reasonable and efficient route planning for takeout riders, an improved ant colony algorithm is proposed from the perspective of takeout riders. Firstly, the ant colony algorithm was used to obtain the initial planning path, and then the large-scale domain search algorithm was used to optimize the initial planning path, and the ant colony algorithm and large-scale domain search algorithm were combined to improve the solution quality. In order to verify the effectiveness of the method, the process of foreign distribution is simulated, and different order quantity scenarios are selected for comparative analysis. According to the optimal distribution roadmap and the optimal value of the target penalty function, it can be concluded that the improved ant colony algorithm is effective, which proves that the proposed improved ant colony algorithm can improve the delivery efficiency of takeout deliverers. The method proposed in this paper can not only improve the intelligent level of delivery, but also put forward a more humanized delivery method from the perspective of delivery riders, which supports the sustainable development of delivery system of Internet connected delivery platform.

    Reference
    [1] 初良勇,闫淼,胡美丽, 等. 生鲜产品物流配送路径优化研究综述及展望[J].物流研究,2021(01):5-13.
    [2] Chu Liangyong, Yan Miao, Hu Meili, et al. Review and prospect of fresh product logistics distribution route optimization [J]. Logistics Research,2021(01):5-13.
    [3] [2] 赵金龙, 蒋忠中, 万明重, 等. 考虑配送截止时间的“货到人”订单拣选优化问题研究[J/OL]. 中国管理科学:1-12[2022-12-08].DOI:10.16381/j.cnki.issn1003-207x.2022.1049.
    [4] Zhao Jinlong, Jiang Zhongzhong, Wan Mingzhong, et al. Research on "Delivery to Person" order Selection and Optimization considering delivery Deadline [J/OL]. China management science: 1-12 [2022-12-08]. DOI: 10.16381 / j.carol carroll nki issn1003-207 - x. 2022.1049.
    [5] [3] 周成昊, 吕博轩, 周翰宇, 等. 以商圈为中心的O2O动态外卖配送路径优化模型与算法[J]. 运筹学学报,2022,26(03):17-30.DOI:10.15960/j.cnki.issn.1007-6093.2022.3.002.
    [6] Zhou Chenghao, Lv Boxuan, Zhou Hanyu, et al. Business circle centered O2O dynamic take-out delivery path optimization model and algorithm [J]. Journal of operational research, 2022, 26 (3) : 17-30. DOI: 10.15960 / j.carol carroll nki. Issn 1007-6093.2022.3.002.
    [7] [4] 刘云忠, 宣慧玉. 车辆路径问题的模型及算法研究综述[J]. 管理工程学报,2005(01):124-130.
    [8] Liu Yunzhong, Xuan Huiyu. Research Summary of Vehicle Routing Problem Model and Algorithm [J]. Journal of Management Engineering,2005(01):124-130. (in Chinese)
    [9] [5] 苏涛, 韩庆田, 李文强, 等. VRP问题蚁群算法研究[J]. 计算机与现代化,2012(11):18-21+25.
    [10] Su Tao, Han Qingtian, Li Wenqiang, et al. Research on Ant Colony Algorithm for VRP Problems [J]. Computer and Modernization,2012(11):18-21+25.
    [11] [6] Xiao Z, Jiang-qing W. Hybrid ant algorithm and applications for vehicle routing problem[J]. Physics Procedia, 2012, 25: 1892-1899.
    [12] [7] 王晓东, 张永强, 薛红. 基于改进蚁群算法对VRP线路优化[J]. 吉林大学学报(信息科学版),2017,35(02):198-203.DOI:10.19292/j.cnki.jdxxp.2017.02.014.
    [13] Wang Xiaodong, Zhang Yongqiang, Xue Hong. Optimization based on improved ant colony algorithm for VRP lines [J]. Journal of jilin university (information science edition), 2017, 35 (02) : 198-203. The DOI: 10.19292 / j.carol carroll nki JDXXP. 2017.02.014.
    [14] [8] Donati A V, Montemanni R, Casagrande N, et al. Time dependent vehicle routing problem with a multi ant colony system[J]. European journal of operational research, 2008, 185(3): 1174-1191.
    [15] [9] Kalayci C B, Kaya C. An ant colony system empowered variable neighborhood search algorithm for the
    [16] vehicle routing problem with simultaneous pickup and delivery[J]. Expert Systems with Applications, 2016, 66: 163-175.
    [17] [10] 李琳, 刘士新, 唐加福. 改进的蚁群算法求解带时间窗的车辆路径问题[J]. 控制与决策,2010,25(09):1379-1383.DOI:10.13195/j.cd.2010.09.102.il.012.
    [18] Li Lin, Liu Shixin, Tang Jiafu. Improved ant colony algorithm for solving vehicle routing problem with time Windows [J]. Control and decision, 2010, 25 (9) : 1379-1383. The DOI: 10.13195 / j.carol carroll d. 2010.09.102.Lil. 012.
    [19] [11] Blum C, Eremeev A, Zakharova Y. Hybridizations of evolutionary algorithms with Large Neighborhood Search[J]. Computer Science Review, 2022, 46: 100512.
    [20] [12] 徐倩, 熊俊, 杨珍花, 等. 基于自适应大邻域搜索算法的外卖配送车辆路径优化[J]. 工业工程与管理,2021,26(03):115-122.DOI:10.19495/j.cnki.1007-5429.2021.03.015.
    [21] Xu Qian, Xiong Jun, Yang Zhenhua, et al. Based on an adaptive large neighborhood search algorithm for take-out delivery vehicle routing optimization [J]. Journal of industrial engineering and management, 2021, 26 (3) : 115-122. The DOI: 10.19495 / j.carol carroll nki. 1007-5429.2021.03.015.
    [22] [13] Natalia C, Triyanti V, Setiawan G, et al. Completion of Capacitated Vehicle Routing Problem (CVRP) and Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) Using Bee Algorithm Approach to Optimize Waste Picking Transportation Problem[J]. Journal of Modern Manufacturing Systems and Technology, 2021, 5(2): 69-77.
    Related
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation
Share
Article Metrics
  • Abstract:397
  • PDF: 0
  • HTML: 0
  • Cited by: 0
History
  • Received:March 11,2023
  • Revised:May 24,2023
  • Adopted:May 25,2023
Article QR Code

Address:No. 219, Ningliu Road, Nanjing, Jiangsu Province

Postcode:210044

Phone:025-58731025