Title: Simulation optimisation of Maicai delivery location using the elbow method and K-means clustering algorithm

Authors: Yonghua Lu; Bingda Zhang; Yuting Shen; Zixia Chen

Addresses: International Business School, Zhejiang Guangsha Vocational and Technical University of Construction, No. 1, Guangfu East Street, Dongyang, Zhejiang, 322100, China ' School of Economics and Management, Shanghai Zhongqiao Vocational and Technical University, No. 3888, Caolanggong Road, Jinshan District, Shanghai, 201514, China ' School of Management and E-Business (Cross-Border E-Commerce College), Zhejiang Gongshang University, No. 18, Xuezheng Street, Qiantang District, Hangzhou, 310018, China ' International Business School, Zhejiang Guangsha Vocational and Technical University of Construction, No. 1, Guangfu East Street, Dongyang, Zhejiang, 322100, China

Abstract: This study proposes an innovative integration of the elbow method and the K-means clustering algorithm to optimise delivery centre locations for Maicai. Based on 55 demand sites, a model is constructed to analyse geographic positions and order volumes. The elbow method is first applied to determine the optimal number of clusters (k), followed by K-means clustering to iteratively optimise delivery centre locations. Python is used to generate a k-S.S.E relationship curve and identify the inflection point automatically. This approach provides data-driven support for determining the optimal number of clusters and uses K-means to perform spatial clustering of demand points. Empirical results demonstrate that the hybrid method effectively reduces logistics costs and enhances delivery efficiency, demonstrating operational efficiency improvements for real-world delivery operations.

Keywords: delivery-centre location; elbow method; K-means clustering; logistics optimisation; delivery efficiency.

DOI: 10.1504/IJCSM.2025.151297

International Journal of Computing Science and Mathematics, 2025 Vol.22 No.4, pp.350 - 361

Received: 08 May 2025
Accepted: 25 Sep 2025
Published online: 22 Jan 2026 *

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