Invulnerability Analysis of Large Logistics Supply Chain Network in Complex Environment Based on the Internet of Things

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The network cascade topology structure of large logistics supply chain and its invulnerability were researched. Logistics supply chain is the combination set of entities such as warehouse, highway, railway, aviation, etc. And its stability of network structure determined the survival and development of the user in the network and supply chain. In order to improve the stability and robustness of logistics supply chain network, an improved cascaded topology structure of large logistics supply chain network with Internet of things in complex environment was proposed based on metric uniform random distribution, and cascading invulnerability of mathematical analysis model was proposed. The probability distribution and critical value of logistics and supply chain network cascading failure was solved in complex environment, and the logistics supply chain network paralysis and damage problems were analyzed from the view of probability and critical value analysis. Take the intelligent car supply chain as the example, simulation result shows that the invulnerability analysis effect is perfect, and the cascading invulnerability critical value is solved precisely, research results provide important theoretical support to realize the intelligent logistic management, it has good application value in the research of logistics supply chain network security and stability.

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2289-2292

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February 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] GUO Qiu-xia, DENG Xiang-ming, OU Yang-jiang. Evaluation of Value Chain Risks Based on BP Artificial Neural Network[J]. Logistics Technology, 2011; 30(7): 120-122. Kroah-Hartman. Linux Device Drivers[M]. Greg O'REILLY &ASSOC INC, 2005, 2: 33-56.

Google Scholar

[2] JIA An- chao, ZHOU Gang. Study on Selection of Suppliers Based on Rough Set and BP Neural Network[J].  Logistics Technology. 2012, 31(12): 229-232.

Google Scholar

[3] MA Jian- hong,JI Li- xia. Study on Agent Immune Network Monitoring System Model[J]. Computer Simulation, 2013, 30(5): 213-216.

Google Scholar

[4] WU Chun-qiong. Network Intrusion Detection Model Based on Feature Selection[J]. Computer Simulation, 2012, 29(6): 136-139.

Google Scholar