A new resilience-based component importance measure for multi-state networks
•A new resilience-based component importance measure for networks is put forth.•The multi-state characteristic of networks is taken account in the new measure.•Both capacity improvement and recovery time of a component are jointly quantified.•A stochastic ranking approach is used to identify importa...
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Veröffentlicht in: | Reliability engineering & system safety 2020-01, Vol.193, p.106591, Article 106591 |
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creator | Xu, Zhaoping Ramirez-Marquez, Jose Emmanuel Liu, Yu Xiahou, Tangfan |
description | •A new resilience-based component importance measure for networks is put forth.•The multi-state characteristic of networks is taken account in the new measure.•Both capacity improvement and recovery time of a component are jointly quantified.•A stochastic ranking approach is used to identify importance rank of components.
Disruptive events such as natural disasters and human errors can have widespread adverse impacts on several networked infrastructures, affecting their functionalities and possibly resulting in large economic losses. It is, therefore, of great significance for these networks to exhibit resilience, defined as the ability of a network to recover from a disruptive event. Inspired by the measures of component importance used in reliability communities, this paper proposes a new resilience-based component importance ranking measure for multi-state networks from the perspective of a post-disaster restoration process. Considering the stochastic nature of disruptive events, the importance measure of each component is evaluated by finding the minimal recovery paths for various disruptive events, and it can be represented by a probability distribution. A stochastic ranking approach is implemented to identify the importance rank of each component in a network. Compared to existing methods, the proposed importance measure not only takes the multi-state characteristics of a network and its components into account but also quantifies the impact of both capacity improvement and recovery time of a component on network resilience. The proposed importance measure is exemplified through case studies in the Seervada Park road network. |
doi_str_mv | 10.1016/j.ress.2019.106591 |
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Disruptive events such as natural disasters and human errors can have widespread adverse impacts on several networked infrastructures, affecting their functionalities and possibly resulting in large economic losses. It is, therefore, of great significance for these networks to exhibit resilience, defined as the ability of a network to recover from a disruptive event. Inspired by the measures of component importance used in reliability communities, this paper proposes a new resilience-based component importance ranking measure for multi-state networks from the perspective of a post-disaster restoration process. Considering the stochastic nature of disruptive events, the importance measure of each component is evaluated by finding the minimal recovery paths for various disruptive events, and it can be represented by a probability distribution. A stochastic ranking approach is implemented to identify the importance rank of each component in a network. Compared to existing methods, the proposed importance measure not only takes the multi-state characteristics of a network and its components into account but also quantifies the impact of both capacity improvement and recovery time of a component on network resilience. The proposed importance measure is exemplified through case studies in the Seervada Park road network.</description><identifier>ISSN: 0951-8320</identifier><identifier>EISSN: 1879-0836</identifier><identifier>DOI: 10.1016/j.ress.2019.106591</identifier><language>eng</language><publisher>Barking: Elsevier Ltd</publisher><subject>Component importance measure ; Component reliability ; Economic conditions ; Economic impact ; Human error ; Measurement methods ; Minimal recovery paths ; Multi-state networks ; Natural disasters ; Network resilience ; Probability distribution ; Ranking ; Recovery time ; Reliability engineering ; Resilience ; Restoration ; Roads ; Stochastic processes ; Stochastic ranking ; Stochasticity ; Transportation networks</subject><ispartof>Reliability engineering & system safety, 2020-01, Vol.193, p.106591, Article 106591</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier BV Jan 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-4576462b061afe838ef6cea7c367b876c01a85d5a1f9e6c86ed8710a6be661bf3</citedby><cites>FETCH-LOGICAL-c372t-4576462b061afe838ef6cea7c367b876c01a85d5a1f9e6c86ed8710a6be661bf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ress.2019.106591$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Xu, Zhaoping</creatorcontrib><creatorcontrib>Ramirez-Marquez, Jose Emmanuel</creatorcontrib><creatorcontrib>Liu, Yu</creatorcontrib><creatorcontrib>Xiahou, Tangfan</creatorcontrib><title>A new resilience-based component importance measure for multi-state networks</title><title>Reliability engineering & system safety</title><description>•A new resilience-based component importance measure for networks is put forth.•The multi-state characteristic of networks is taken account in the new measure.•Both capacity improvement and recovery time of a component are jointly quantified.•A stochastic ranking approach is used to identify importance rank of components.
Disruptive events such as natural disasters and human errors can have widespread adverse impacts on several networked infrastructures, affecting their functionalities and possibly resulting in large economic losses. It is, therefore, of great significance for these networks to exhibit resilience, defined as the ability of a network to recover from a disruptive event. Inspired by the measures of component importance used in reliability communities, this paper proposes a new resilience-based component importance ranking measure for multi-state networks from the perspective of a post-disaster restoration process. Considering the stochastic nature of disruptive events, the importance measure of each component is evaluated by finding the minimal recovery paths for various disruptive events, and it can be represented by a probability distribution. A stochastic ranking approach is implemented to identify the importance rank of each component in a network. Compared to existing methods, the proposed importance measure not only takes the multi-state characteristics of a network and its components into account but also quantifies the impact of both capacity improvement and recovery time of a component on network resilience. The proposed importance measure is exemplified through case studies in the Seervada Park road network.</description><subject>Component importance measure</subject><subject>Component reliability</subject><subject>Economic conditions</subject><subject>Economic impact</subject><subject>Human error</subject><subject>Measurement methods</subject><subject>Minimal recovery paths</subject><subject>Multi-state networks</subject><subject>Natural disasters</subject><subject>Network resilience</subject><subject>Probability distribution</subject><subject>Ranking</subject><subject>Recovery time</subject><subject>Reliability engineering</subject><subject>Resilience</subject><subject>Restoration</subject><subject>Roads</subject><subject>Stochastic processes</subject><subject>Stochastic ranking</subject><subject>Stochasticity</subject><subject>Transportation networks</subject><issn>0951-8320</issn><issn>1879-0836</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kE9LxDAQxYMouK5-AU8Fz10nyTZJwcuy-A8WvOg5pOkUUrfNmqSK396s9exphpn33gw_Qq4prChQcduvAsa4YkDrPBBVTU_IgipZl6C4OCULqCtaKs7gnFzE2APAuq7kguw2xYhfRXa7vcPRYtmYiG1h_XDwI46pcLkJyeRVMaCJU8Ci86EYpn1yZUwmYU5IXz68x0ty1pl9xKu_uiRvD_ev26dy9_L4vN3sSsslS-W6kmItWAOCmg4VV9gJi0ZaLmSjpLBAjaraytCuRmGVwFZJCkY0KARtOr4kN3PuIfiPCWPSvZ_CmE9qxjkAY7zmWcVmlQ0-xoCdPgQ3mPCtKegjNd3rIzV9pKZnatl0N5sw___pMOhof7m0LqBNuvXuP_sPkBt2eA</recordid><startdate>202001</startdate><enddate>202001</enddate><creator>Xu, Zhaoping</creator><creator>Ramirez-Marquez, Jose Emmanuel</creator><creator>Liu, Yu</creator><creator>Xiahou, Tangfan</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>SOI</scope></search><sort><creationdate>202001</creationdate><title>A new resilience-based component importance measure for multi-state networks</title><author>Xu, Zhaoping ; Ramirez-Marquez, Jose Emmanuel ; Liu, Yu ; Xiahou, Tangfan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-4576462b061afe838ef6cea7c367b876c01a85d5a1f9e6c86ed8710a6be661bf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Component importance measure</topic><topic>Component reliability</topic><topic>Economic conditions</topic><topic>Economic impact</topic><topic>Human error</topic><topic>Measurement methods</topic><topic>Minimal recovery paths</topic><topic>Multi-state networks</topic><topic>Natural disasters</topic><topic>Network resilience</topic><topic>Probability distribution</topic><topic>Ranking</topic><topic>Recovery time</topic><topic>Reliability engineering</topic><topic>Resilience</topic><topic>Restoration</topic><topic>Roads</topic><topic>Stochastic processes</topic><topic>Stochastic ranking</topic><topic>Stochasticity</topic><topic>Transportation networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Zhaoping</creatorcontrib><creatorcontrib>Ramirez-Marquez, Jose Emmanuel</creatorcontrib><creatorcontrib>Liu, Yu</creatorcontrib><creatorcontrib>Xiahou, Tangfan</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Environment Abstracts</collection><jtitle>Reliability engineering & system safety</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Zhaoping</au><au>Ramirez-Marquez, Jose Emmanuel</au><au>Liu, Yu</au><au>Xiahou, Tangfan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A new resilience-based component importance measure for multi-state networks</atitle><jtitle>Reliability engineering & system safety</jtitle><date>2020-01</date><risdate>2020</risdate><volume>193</volume><spage>106591</spage><pages>106591-</pages><artnum>106591</artnum><issn>0951-8320</issn><eissn>1879-0836</eissn><abstract>•A new resilience-based component importance measure for networks is put forth.•The multi-state characteristic of networks is taken account in the new measure.•Both capacity improvement and recovery time of a component are jointly quantified.•A stochastic ranking approach is used to identify importance rank of components.
Disruptive events such as natural disasters and human errors can have widespread adverse impacts on several networked infrastructures, affecting their functionalities and possibly resulting in large economic losses. It is, therefore, of great significance for these networks to exhibit resilience, defined as the ability of a network to recover from a disruptive event. Inspired by the measures of component importance used in reliability communities, this paper proposes a new resilience-based component importance ranking measure for multi-state networks from the perspective of a post-disaster restoration process. Considering the stochastic nature of disruptive events, the importance measure of each component is evaluated by finding the minimal recovery paths for various disruptive events, and it can be represented by a probability distribution. A stochastic ranking approach is implemented to identify the importance rank of each component in a network. Compared to existing methods, the proposed importance measure not only takes the multi-state characteristics of a network and its components into account but also quantifies the impact of both capacity improvement and recovery time of a component on network resilience. The proposed importance measure is exemplified through case studies in the Seervada Park road network.</abstract><cop>Barking</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.ress.2019.106591</doi><oa>free_for_read</oa></addata></record> |
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subjects | Component importance measure Component reliability Economic conditions Economic impact Human error Measurement methods Minimal recovery paths Multi-state networks Natural disasters Network resilience Probability distribution Ranking Recovery time Reliability engineering Resilience Restoration Roads Stochastic processes Stochastic ranking Stochasticity Transportation networks |
title | A new resilience-based component importance measure for multi-state networks |
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