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Maximum flow-based resilience analysis: From component to system

Resilience, the ability to withstand disruptions and recover quickly, must be considered during system design because any disruption of the system may cause considerable loss, including economic and societal. This work develops analytic maximum flow-based resilience models for series and parallel sy...

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Detalles Bibliográficos
Autores principales: Jin, Chong, Li, Ruiying, Kang, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5435244/
https://www.ncbi.nlm.nih.gov/pubmed/28545135
http://dx.doi.org/10.1371/journal.pone.0177668
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author Jin, Chong
Li, Ruiying
Kang, Rui
author_facet Jin, Chong
Li, Ruiying
Kang, Rui
author_sort Jin, Chong
collection PubMed
description Resilience, the ability to withstand disruptions and recover quickly, must be considered during system design because any disruption of the system may cause considerable loss, including economic and societal. This work develops analytic maximum flow-based resilience models for series and parallel systems using Zobel’s resilience measure. The two analytic models can be used to evaluate quantitatively and compare the resilience of the systems with the corresponding performance structures. For systems with identical components, the resilience of the parallel system increases with increasing number of components, while the resilience remains constant in the series system. A Monte Carlo-based simulation method is also provided to verify the correctness of our analytic resilience models and to analyze the resilience of networked systems based on that of components. A road network example is used to illustrate the analysis process, and the resilience comparison among networks with different topologies but the same components indicates that a system with redundant performance is usually more resilient than one without redundant performance. However, not all redundant capacities of components can improve the system resilience, the effectiveness of the capacity redundancy depends on where the redundant capacity is located.
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spelling pubmed-54352442017-05-26 Maximum flow-based resilience analysis: From component to system Jin, Chong Li, Ruiying Kang, Rui PLoS One Research Article Resilience, the ability to withstand disruptions and recover quickly, must be considered during system design because any disruption of the system may cause considerable loss, including economic and societal. This work develops analytic maximum flow-based resilience models for series and parallel systems using Zobel’s resilience measure. The two analytic models can be used to evaluate quantitatively and compare the resilience of the systems with the corresponding performance structures. For systems with identical components, the resilience of the parallel system increases with increasing number of components, while the resilience remains constant in the series system. A Monte Carlo-based simulation method is also provided to verify the correctness of our analytic resilience models and to analyze the resilience of networked systems based on that of components. A road network example is used to illustrate the analysis process, and the resilience comparison among networks with different topologies but the same components indicates that a system with redundant performance is usually more resilient than one without redundant performance. However, not all redundant capacities of components can improve the system resilience, the effectiveness of the capacity redundancy depends on where the redundant capacity is located. Public Library of Science 2017-05-17 /pmc/articles/PMC5435244/ /pubmed/28545135 http://dx.doi.org/10.1371/journal.pone.0177668 Text en © 2017 Jin et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jin, Chong
Li, Ruiying
Kang, Rui
Maximum flow-based resilience analysis: From component to system
title Maximum flow-based resilience analysis: From component to system
title_full Maximum flow-based resilience analysis: From component to system
title_fullStr Maximum flow-based resilience analysis: From component to system
title_full_unstemmed Maximum flow-based resilience analysis: From component to system
title_short Maximum flow-based resilience analysis: From component to system
title_sort maximum flow-based resilience analysis: from component to system
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5435244/
https://www.ncbi.nlm.nih.gov/pubmed/28545135
http://dx.doi.org/10.1371/journal.pone.0177668
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