Conference proceeding
Topological Resilience Analysis of Supply Networks under Random Disruptions and Targeted Attacks
PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015), pp.250-257
08/25/2015
DOI: 10.1145/2808797.2809325
Abstract
Along with the rapid advancement of information technology, the traditional hierarchical supply chain has been quickly evolving into a variety of supply networks, which usually incorporate a large number of entities into complex graph topologies. The study of the resilience of supply networks is an important challenge. In this paper, we exploit the resilience embedded in the network topology by investigating in depth the multiple-path reachability of each demand node to other nodes, and propose a novel network resilience metric. We also develop new supply-network growth mechanisms that reflect the heterogeneous roles of different types of nodes in the supply network. We incorporate them into two fundamental network topologies (i.e. random-graph network and scale-free network), and evaluate their resilience against both random disruptions and targeted attacks using the new resilience metric. The experimental results verify the validity of our resilience metric and the effectiveness of our growth model. This research provides a generic framework and important insights into the construction and resilience analysis of complex supply networks.
Details
- Title: Subtitle
- Topological Resilience Analysis of Supply Networks under Random Disruptions and Targeted Attacks
- Creators
- Wenjun Wang - University of IowaW. Nick Street - University of IowaRenato E. deMatta - University of Iowa
- Contributors
- J Pei (Editor)F Silvestri (Editor)J Tang (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015), pp.250-257
- Publisher
- Assoc Computing Machinery
- DOI
- 10.1145/2808797.2809325
- Number of pages
- 8
- Language
- English
- Date published
- 08/25/2015
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984380519102771
Metrics
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