Multi-level Logistics Network Node Siting Model Based on K-Means

DOI

10.2991/978-94-6463-222-4_47How to use a DOI?

Keywords

Artificial Intelligence; K-Means; Weighted Average; Silhouette Coefficient

Abstract

Artificial intelligence plays a pivotal role in global logistics and supply chain management. AI enables real-time optimization for routes and minimizes wastage while increasing delivery efficiency. Companies can leverage AI in logistics to optimize resources and be more efficient. Currently, the node siting of logistics transportation networks faces two challenges. (1) The unreasonable selection of upstream and downstream nodes on the logistics transportation network can result in the wastage of transportation costs. (2) The siting of nodes in logistics transportation networks just takes the effect of geography into account and ignores other aspects that may have an impact on transportation efficiency. To tackle these challenges, we design a multi-level logistics network node siting model based on K-Means. Firstly, we adopt a space first and then attribute clustering strategy to cluster according to the spatial coordinates of each community to obtain the initial position of each distribution node, and optimize the siting based on the weight of express delivery in each community. Secondly, we determine the location of transit nodes and large distribution centers in turn according to the siting of distribution nodes. Taking Erdao District, Changchun City as an example, we finally build a logistics transportation network composed of 3 large distribution centers, 11 transit nodes and 42 distribution nodes using this model. This model can effectively solve the problem of node siting in multi-level logistics networks and provide a reference for future siting planning of logistics enterprises .

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jie Liu
AU  - Shuang Tian
AU  - Qingqing Wang
AU  - Chenguang Zhang
AU  - Min Ning
AU  - Changlong Li
PY  - 2023
DA  - 2023/08/28
TI  - Multi-level Logistics Network Node Siting Model Based on K-Means
BT  - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
PB  - Atlantis Press
SP  - 437
EP  - 444
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-222-4_47
DO  - 10.2991/978-94-6463-222-4_47
ID  - Liu2023
ER  -