Ma, Z.; Zheng, X.; Liang, H.; Luo, P. Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion. Sensors2024, 24, 1878.
Ma, Z.; Zheng, X.; Liang, H.; Luo, P. Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion. Sensors 2024, 24, 1878.
Ma, Z.; Zheng, X.; Liang, H.; Luo, P. Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion. Sensors2024, 24, 1878.
Ma, Z.; Zheng, X.; Liang, H.; Luo, P. Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion. Sensors 2024, 24, 1878.
Abstract
The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the corporate distribution image, and further reduce corporate operating costs and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identified the locations of primary and secondary logistics nodes but also provided a new perspective for constructing the urban last-mile logistics system, enriching the academic research related to logistics node construction. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.
Keywords
Last mile; Logistics nodes; Clustering analysis; Greedy algorithm
Subject
Business, Economics and Management, Business and Management
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
