Biblio

The supply chain network is a complex network with the risk of cascading failure. To study the cascading failure in it, an accurate supply chain network model needs to be established. In this paper, we construct a layered supply chain network model according to the types of companies in real supply chain networks. We first define the similarity between companies in the same layer by studying real-world scenarios in supply chain networks. Then, considering both the node degree and the similarity between nodes in the same layer, we propose preferential attachment probability formulas for the new nodes to join the exist network. Finally, the evolution steps of the model are summarized. We analyze the structural characteristics of the new model. The results show that the new model has scale-free property and small-world property, which conform to the structural characteristics of the known supply chain networks. Compared with the other network models, it is found that the new model can better describe the actual supply chain network.

With the development of globalization, more and more enterprises are involved in the supply chain network with increasingly complex structure. In this paper, enterprises and relations in the logistics network are abstracted as nodes and edges of the complex network. A graph model for a supply chain network to specified industry is constructed, and the Neo4j graph database is employed to store the graph data. This paper uses the theoretical research tool of complex network to model and analyze the supply chain, and designs a supply chain network evaluation system which include static and dynamic measurement indexes according to the statistical characteristics of complex network. In this paper both the static and dynamic resilience characteristics of the the constructed supply chain network are evaluated from the perspective of complex network. The numeric experimental simulations are conducted for validation. This research has practical and theoretical significance for enterprises to make strategies to improve the anti-risk capability of supply chain network based on logistics network information.

This paper firstly defines the importance index of several types of nodes from the local and global attributes of the supply chain network, analyzes the propagation effect of the nodes after the risk is generated from the perspective of the network topology, and forms multidimensional structural attributes that describe node risk propagation capabilities of the supply chain network. Then the indicators of the structure attributes of the supply chain network are simplified based on PCA (Principal Component Analysis). Finally, a risk assessment model of node risk propagation is constructed using BP neural network. This paper also takes 4G smart phone industry chain data as an example to verify the validity of the proposed model.

In this paper, an optimization model of automobile supply chain network with risks under fuzzy price is put forward. The supply chain network is composed of component suppliers, plants, and distribution centers. The total costs of automobile supply chain consist of variable costs, fixed costs, and transportation costs. The objective of this study is to minimize the risks of total profits. In order to deal with this model, this paper puts forward an approximation method to transform a continuous fuzzy problem into discrete fuzzy problem. The model is solved using Cplex 12.6. The results show that Cplex 12.6 can perfectly solve this model, the expected value and lower semi-variance of total profits converge with the increasing number of discretization points, the structure of automobile supply chain network keeps unchanged with the increasing number of discretization points.