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Matthias Winkenbach. Rueda Guerrero, María Ximena Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-12-18T19:47:09Z 2018-12-18T19:47:09Z 2018 2018 http://hdl.handle.net/1721.1/119719 1078637734 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 65-67). In this thesis, we study the robustness of complex supply chain systems from a network science perspective. Through the simulation of targeted attacks to nodes and edges using different hierarchical measures from network science to select the most relevant components, we evaluate the extent to which local centrality measures can estimate the relevance of a node in maintaining the connectivity and the efficient communication across the network. We perform the experiments on two real-world supply chain data sets, and on an ensemble of networks generated from network growth models that share simple topological properties with the real-world networks. It is found that all models produce more robust networks than the data sets of choice. In addition, the removal of high average neighbor degree nodes seems to have little impact on the connectivity of the network, and a highly varying impact on the efficiency of the network. Finally, robustness against targeted node and edge removal is found to be more associated to the number of nodes and links in the network than to more complex network measures such as the degree distribution. by María Ximena Rueda Guerrero. M.Eng. 67 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Robustness of complex supply chain networks to targeted attacks Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="fd273423-84b1-48c3-9911-3bcdbf2a9faa"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Robustness of complex supply chain networks to targeted attacks</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Rueda Guerrero, María Ximena</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>In this thesis, we study the robustness of complex supply chain systems from a network science perspective. Through the simulation of targeted attacks to nodes and edges using different hierarchical measures from network science to select the most relevant components, we evaluate the extent to which local centrality measures can estimate the relevance of a node in maintaining the connectivity and the efficient communication across the network. We perform the experiments on two real-world supply chain data sets, and on an ensemble of networks generated from network growth models that share simple topological properties with the real-world networks. It is found that all models produce more robust networks than the data sets of choice. In addition, the removal of high average neighbor degree nodes seems to have little impact on the connectivity of the network, and a highly varying impact on the efficiency of the network. Finally, robustness against targeted node and edge removal is found to be more associated to the number of nodes and links in the network than to more complex network measures such as the degree distribution.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>