Research on Logistics Warehouse Planning Based on K-Means Algorithm Clustering Analysis

Hongwei Li, Linbo Su, Faming Da, Lei Qin

2022

Abstract

With the development of logistics industry, warehouse location planning has become a key link in logistics network layout, and its rationality has an important impact on logistics cost and service level. K-means algorithm is efficient and fast for analyzing and processing large sample data, but the randomness of K value will lead to the reduction of clustering effect. In this paper, the optimal K value is selected by clustering evaluation index CH, so as to improve the K-means algorithm. Through simulation, the optimal distribution area is obtained, and then the center of gravity method is used for site selection. Finally, the actual location of the warehouse is determined by combining the natural environment and infrastructure factors. The results show that the improved K-Means clustering algorithm has practical significance for the planning of logistics warehouse.

Paper Citation

in Harvard Style

Li H., Su L., Da F. and Qin L. (2022). Research on Logistics Warehouse Planning Based on K-Means Algorithm Clustering Analysis. In Proceedings of the 4th International Conference on Economic Management and Model Engineering - Volume 1: ICEMME; ISBN 978-989-758-636-1, SciTePress, pages 445-450. DOI: 10.5220/0012034400003620

in Bibtex Style

@conference{icemme22,
author={Hongwei Li and Linbo Su and Faming Da and Lei Qin},
title={Research on Logistics Warehouse Planning Based on K-Means Algorithm Clustering Analysis},
booktitle={Proceedings of the 4th International Conference on Economic Management and Model Engineering - Volume 1: ICEMME},
year={2022},
pages={445-450},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012034400003620},
isbn={978-989-758-636-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Economic Management and Model Engineering - Volume 1: ICEMME
TI - Research on Logistics Warehouse Planning Based on K-Means Algorithm Clustering Analysis
SN - 978-989-758-636-1
AU - Li H.
AU - Su L.
AU - Da F.
AU - Qin L.
PY - 2022
SP - 445
EP - 450
DO - 10.5220/0012034400003620
PB - SciTePress