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Research on Location and Path Planning of Distribution Center Based on Improved k-Means Clustering Algorithm and Improved Ant Colony Algorithm

In: Liss 2020

Author

Listed:
  • Shuihai Dou

    (Beijing Institute of Graphic Communication)

  • Zhou Yao

    (Beijing Jiaotong University)

  • Xiaotong Shi

    (Beijing Institute of Graphic Communication)

  • Guanyi Liu

    (Beijing Institute of Graphic Communication)

Abstract

In the complex logistics terminal distribution link, the needs of user points and the road level between user points have an important impact on the location of the distribution center and vehicle path planning. The traditional k-means clustering algorithm and ant colony algorithm have some problems in the process of site selection and vehicle route planning in the distribution center, such as the initial cluster center selection is random, and the needs of user points are not considered. It cannot meet the actual logistics terminal distribution requirements. In order to solve the problems of traditional k-means clustering algorithm and ant colony algorithm, in the ant colony algorithm, the center of gravity method is introduced to obtain the initial cluster center, and then the new cluster center calculation method is obtained by introducing the user point demand. In the ant colony algorithm, the parameters are improved by introducing the driving speed of the vehicle, and then the new ant probability calculation formula is obtained by introducing the demand of the user point. Finally, a simulation experiment was conducted through MATLAB. Experimental results show that the improved k-means clustering algorithm and ant colony algorithm are feasible and effective in the logistics end distribution link.

Suggested Citation

  • Shuihai Dou & Zhou Yao & Xiaotong Shi & Guanyi Liu, 2021. "Research on Location and Path Planning of Distribution Center Based on Improved k-Means Clustering Algorithm and Improved Ant Colony Algorithm," Springer Books, in: Shifeng Liu & Gábor Bohács & Xianliang Shi & Xiaopu Shang & Anqiang Huang (ed.), Liss 2020, pages 797-809, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-4359-7_55
    DOI: 10.1007/978-981-33-4359-7_55
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