Modeling and Numerical Methods of Supply Chain Trust Network with the Complex Network

被引:2
|
作者
Zhang, Xuelong [1 ,2 ]
Wang, Hui [2 ]
Nan, Jiangxia [3 ]
Luo, Yuxi [1 ]
Yi, Yanling [4 ]
机构
[1] Guangxi Normal Univ, Sch Econ & Management, Guilin 541004, Peoples R China
[2] Guilin Univ Elect Technol, Sch Business, Guilin 541004, Peoples R China
[3] Suzhou Univ Sci & Technol, Sch Business, Suzhou 215009, Peoples R China
[4] Univ Minnesota, Carlson Sch Management, Minneapolis, MN 55455 USA
来源
SYMMETRY-BASEL | 2022年 / 14卷 / 02期
基金
中国国家自然科学基金;
关键词
complex network; supply chain trust; analytic network process; trust evaluation model; numerical methods; PERFORMANCE; OPTIMIZATION; RESILIENCE; ALGORITHM;
D O I
10.3390/sym14020235
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Finding reliable partners is the key to supply chain management. However, the symmetrical evaluation of enterprise trust is complex, so the decision-makers must understand its quantitative and qualitative characteristics in order to realize a reasonable evaluation. Based on the analysis of the causes and influencing factors of supply chain trust, this paper constructed four primary indexes and 16 secondary indexes to define enterprise trust, and used analytic network process (ANP) to evaluate and rank the indicators. Then, the paper constructed a supply chain directed weighted trust evolution network model based on complex network theory, integrated trust into the network with edge weights, and put forward the merit index of comprehensive node degree, weight, and efficiency to study the supply chain network evolution. The simulation results show that the node degree distribution in the trust evolution network conforms to the power-law distribution rule, and the trust evolution model of the complex network has obvious scale-free characteristics, which effectively avoid the situation that the node influence is too high due to the excessive strength of a single index. At the same time, it can quickly evaluate the node influence of the directed weighted complex network, and provide certain practical value for the node trust prediction of the supply chain network.
引用
收藏
页数:20
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