scholarly journals Research on Vehicle Scheduling Optimization Problem of Dangerous Goods Transportation under Single Distribution Center based on Security

2014 ◽  
Author(s):  
Kun Zou ◽  
Jin Du
Keyword(s):  
Optimization Problem ◽  
Distribution Center ◽  
Vehicle Scheduling ◽  
Scheduling Optimization ◽  
Dangerous Goods

scholarly journals A Customer Clustering Algorithm for Power Logistics Distribution Network Structure and Distribution Volume Constraints

2021 ◽  
Vol 15 ◽  
pp. 1051-1056
Author(s):  
Jianying Zhong ◽  
Jibin Zhu ◽  
Yonghao Guo ◽  
Yunxin Chang ◽  
Chaofeng Zhu
Keyword(s):  
Network Structure ◽  
Clustering Algorithm ◽  
Distribution Network ◽  
Distribution Volume ◽  
Distribution Center ◽  
Vehicle Scheduling ◽  
Route Optimization ◽  
Scheduling Optimization ◽  
Logistics Distribution ◽  
Center Location

Customer clustering technology for distribution process is widely used in location selection, distribution route optimization and vehicle scheduling optimization of power logistics distribution center. Aiming at the problem of customer clustering with unknown distribution center location, this paper proposes a clustering algorithm considering distribution network structure and distribution volume constraint, which makes up for the defect that the classical Euclidean distance does not consider the distribution road information. This paper proposes a logistics distribution customer clustering algorithm, which improves CLARANS algorithm to make the clustering results meet the constraints of customer distribution volume. By using the single vehicle load rate, the sufficient conditions for logistics distribution customer clustering to be solvable under the condition of considering the ubiquitous and constraints are given, which effectively solves the problem of logistics distribution customer clustering with sum constraints. The results state clearly that the clustering algorithm can effectively deal with large-scale spatial data sets, and the clustering process is not affected by isolated customers, The clustering results can be effectively applied to the distribution center location, distribution cost optimization, distribution route optimization and distribution area division of vehicle scheduling optimization.

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