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基于城市地理信息融合视角的物流中心选址与物流网络构建

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion.

作者信息

Ma Zhanxin, Zheng Xiyu, Liang Hejun, Luo Ping

机构信息

China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China.

College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China.

出版信息

Sensors (Basel). 2024 Mar 14;24(6):1878. doi: 10.3390/s24061878.

DOI:10.3390/s24061878
PMID:38544141
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10975417/
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 image of corporate distribution, 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 identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. 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.

摘要

城市的最后一英里物流已成为城市物流系统不可或缺的一部分。本研究旨在探索最后一英里物流节点的有效选择,以提高物流配送效率,强化企业配送形象,进一步降低企业运营成本,并缓解城市交通拥堵。本文提出一种基于聚类的方法,从地理信息融合的角度识别城市物流节点。该方法综合考虑了物流配送的覆盖范围、均衡性和城市交通状况等几个关键指标。此外,我们采用贪婪算法来识别主节点周围的次节点,从而构建一个有效的节点网络。为验证该模型的实用性,我们利用南京仙林地区的物流需求和交通状况进行了实证模拟研究。本研究不仅确定了主、次物流节点的位置,还为构建城市最后一英里物流系统提供了新的视角,丰富了与物流节点建设相关的学术研究。本研究结果对于优化城市物流网络、提高物流效率以及促进城市交通状况改善具有重要的理论和现实意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd8a/10975417/9552f5309a82/sensors-24-01878-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd8a/10975417/785f2fe0a55e/sensors-24-01878-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd8a/10975417/4a78c2f5128c/sensors-24-01878-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd8a/10975417/ccc6cff16516/sensors-24-01878-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd8a/10975417/51a1fb2a0b6c/sensors-24-01878-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd8a/10975417/9552f5309a82/sensors-24-01878-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd8a/10975417/785f2fe0a55e/sensors-24-01878-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd8a/10975417/4a78c2f5128c/sensors-24-01878-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd8a/10975417/ccc6cff16516/sensors-24-01878-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd8a/10975417/51a1fb2a0b6c/sensors-24-01878-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd8a/10975417/9552f5309a82/sensors-24-01878-g005.jpg

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2
Super ensemble based streamflow simulation using multi-source remote sensing and ground gauged rainfall data fusion.基于超级集成的径流模拟:利用多源遥感和地面实测降雨数据融合
Heliyon. 2023 Jul 6;9(7):e17982. doi: 10.1016/j.heliyon.2023.e17982. eCollection 2023 Jul.
3
A Soft Sensor Model of Sintering Process Quality Index Based on Multi-Source Data Fusion.
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4
Soil carbon content prediction using multi-source data feature fusion of deep learning based on spectral and hyperspectral images.基于光谱和高光谱图像的深度学习多源数据特征融合的土壤碳含量预测。
Chemosphere. 2023 Sep;336:139161. doi: 10.1016/j.chemosphere.2023.139161. Epub 2023 Jun 9.
5
All-parameter calibration method of the on-orbit multi-view dynamic photogrammetry system.星载多视动态摄影测量系统全参数标定方法。
Opt Express. 2023 Mar 27;31(7):11471-11489. doi: 10.1364/OE.482386.
6
Real-Time Target Detection System for Intelligent Vehicles Based on Multi-Source Data Fusion.基于多源数据融合的智能车辆实时目标检测系统。
Sensors (Basel). 2023 Feb 6;23(4):1823. doi: 10.3390/s23041823.
7
A branch-and-Benders-cut algorithm for a bi-objective stochastic facility location problem.一种用于双目标随机设施选址问题的分支定界-割平面算法。
OR Spectr. 2022;44(2):419-459. doi: 10.1007/s00291-020-00616-7. Epub 2021 Mar 6.
8
Multi-objective cold chain logistic distribution center location based on carbon emission.基于碳排放的多目标冷链物流配送中心选址
Environ Sci Pollut Res Int. 2021 Feb 23. doi: 10.1007/s11356-021-12992-w.