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Quantitative Robustness Index Design for Supply Chain Networks

  • Ming Dong
  • F. Frank Chen
Chapter
Part of the Springer Series in Advanced Manufacturing book series (SSAM)

Abstract

As an event-driven system, a supply chain network will face uncertainties inside the supply chain and also unexpected events outside the supply chain network such as contingency, disruption, and disaster. These uncertainties and unexpected events have negative impacts on the survival and performance of supply chain networks. As a dynamic system, a supply chain network will evolve over time. Nodes and links may be added and deleted, or part of networks can be disconnected. The functional performance of nodes and links of supply chain networks may deteriorate quickly under unexpected events. Providing resilient capability against failures is a critical issue for supply chain networks since a single failure may propagate along the chain and cause a series of severe losses in network performance and structure. Robustness of a supply chain network is an important research issue, a vulnerable supply chain network may not be able to operate at all. The goal of this chapter is to investigate the relationship between robustness metrics and basic network parameters. A systemwide approach is presented to quantifying the robustness of supply chain networks. This approach considers both network structural and network functional parameters. Metagraphs are employed to calculate the structural robustness of nodes, and a topological index is used to capture the robustness of the overall network structure. Finally, the integrated systemwide network robustness index can be obtained by the proposed algorithm. Several hypothetical supply chain networks are employed to demonstrate the proposed approach.

Keywords

Supply Chain Adjacency Matrix Unexpected Event Topological Index Supply Chain Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • Ming Dong
    • 1
  • F. Frank Chen
    • 2
  1. 1.Department of Industrial Engineering and Management, School of Mechanical EngineeringShanghai Jiao Tong UniversityP.R. China
  2. 2.Grado Department of Industrial and Systems EngineeringVirginia Polytechnic Institute and State UniversityUSA

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