Research on hub-and-spoke logistics network construction: An empirical analysis from Zhejiang Province in China

Author(s):  
Daqiang Chen ◽  
Shaoyu Li ◽  
Fengying Qiu ◽  
Nafei Xu
2021 ◽  
Vol 7 (6) ◽  
pp. 5340-5353
Author(s):  
Wang Bangjun ◽  
Wei Yixi ◽  
Ji Feng ◽  
Zhu Wei ◽  
Yu Pan

Objectives: The logistics hub construction has always been the short board of logistics network planning in China. In order to improve the decision-making efficiency of logistics enterprise’s hub selection and reduce its comprehensive operation cost, this paper establishes a cost difference model for hub-and-spoke(H-S) and point-to-point(P-P) networks considering the fixed cost of hubs, transportation and route costs based on the 0-1 integer nonlinear programming. The model aims at minimizing the cost difference between the two networks, and divides the fixed cost of the hubs into three situations: full lease, lease and self-built, and fully self-built. Finally, this paper takes tobacco transportation logistics as an example, and use particle swarm algorithm to solve the model by using tobacco transportation logistics data of a logistics enterprise in Jiangsu Province. The results show that: (i) in the case of complete leasing, the total cost of the H-S network decreases with the increase of the number of hubs, and the cost change has a point of intersection with the total cost of the P-P network;(ii) when the lease and self-build are mixed, the increase is first reduced and then increased, it is U-shaped and has a minimum value, and there are two intersections with the total cost of the P-P network;(iii) the situation of completely self-built and fully leased is just the opposite. This paper takes tobacco transportation logistics as a representative, and provides a reference for logistics companies to choose the appropriate regional logistics network structure and different pivot points.


2019 ◽  
Vol 12 (2) ◽  
pp. 356
Author(s):  
Jingjing Hu ◽  
Youfang Huang

Purpose: The overstocked goods flow in the hub of hub-and-spoke logistics network should be disposed of in time, to reduce delay loss and improve the utilization rate of logistics network resources. The problem we need to solve is to let logistics network cooperate by sharing network resources to shunt goods from one hub-and-spoke network to another hub-and-spoke network.Design/methodology/approach: This paper proposes the hub shunting cooperation between two hub-and-spoke networks. Firstly, a hybrid integer programming model was established to describe the problem, and then a multi-layer genetic algorithm was designed to solve it and two hub-and-spoke networks are expressed by different gene segments encoded by genes. The network data of two third-party logistics companies in southern and northern China are used for example analysis at the last step. Findings: The hub-and-spoke networks of the two companies were constructed simultaneously. The transfer cost coefficient between two networks and the volume of cargo flow in the network have an impact on the computation of hubs that needed to be shunt and the corresponding cooperation hubs in the other network.Originality/value: Previous researches on hub-and-spoke logistics network focus on one logistics network, while we study the cooperation and interaction between two hub-and-spoke networks. It shows that two hub-and-spoke network can cooperate across the network to shunt the goods in the hub and improve the operation efficiency of the logistics network. 


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Junjun Ye ◽  
Jijian Wang ◽  
Zhiwei Zhu

This paper aims to reveal the relationship between intellectual property protection (IPP) and industrial transformation and upgrading (T&U) in southeastern China’s Zhejiang Province. Taking five representative industries as objects, the shift-share analysis was adopted to measure the T&U level of each industry, with the total output in 2004–2019 as the basis. The results show that wholesale and retail, lodging and catering, finance, and real estate are the four advantageous industries. Further, the authors calculated Pearson’s correlation coefficients between IPP intensity and the T&U levels of the four industries. By the coefficients, the four industries can be ranked in descending order as lodging and catering (0.8743), real estate (0.6908), wholesale and retail (0.5891), and finance (0.5468). In addition, the IPP was found to be positively correlated with total manufacturing output (0.8027). Hence, the IPP can significantly promote the development of these five industries.


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