Stochastic measures of resilience and their application to container terminals
Abstract While early research efforts were devoted to the protection of systems against disruptive events, be they malevolent attacks, man-made accidents, or natural disasters, recent attention has been given to the resilience, or the ability of systems to "bounce back," of these events. D...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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2014
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| _version_ | 1826261405588783104 |
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| author | Pant, R Barker, K Ramirez-Marquez, J Rocco, C |
| author_facet | Pant, R Barker, K Ramirez-Marquez, J Rocco, C |
| author_sort | Pant, R |
| collection | OXFORD |
| description | Abstract While early research efforts were devoted to the protection of systems against disruptive events, be they malevolent attacks, man-made accidents, or natural disasters, recent attention has been given to the resilience, or the ability of systems to "bounce back," of these events. Discussed here is a modeling paradigm for quantifying system resilience, primarily as a function of vulnerability (the adverse initial system impact of the disruption) and recoverability (the speed of system recovery). To account for uncertainty, stochastic measures of resilience are introduced, including Time to Total System Restoration, Time to Full System Service Resilience, and Time to α%-Resilience. These metrics are applied to quantify the resilience of inland waterway ports, important hubs in the flow of commodities, and the port resilience approach is deployed in a data-driven case study for the inland Port of Catoosa in Oklahoma. The contributions herein demonstrate a starting point in the development of a resilience decision making framework. © 2014 Elsevier Ltd. All rights reserved. |
| first_indexed | 2024-03-06T19:20:55Z |
| format | Journal article |
| id | oxford-uuid:1a0ebed4-ec33-44ba-b5fd-a9a9b605a8bd |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:20:55Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | oxford-uuid:1a0ebed4-ec33-44ba-b5fd-a9a9b605a8bd2022-03-26T10:52:37ZStochastic measures of resilience and their application to container terminalsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:1a0ebed4-ec33-44ba-b5fd-a9a9b605a8bdEnglishSymplectic Elements at Oxford2014Pant, RBarker, KRamirez-Marquez, JRocco, CAbstract While early research efforts were devoted to the protection of systems against disruptive events, be they malevolent attacks, man-made accidents, or natural disasters, recent attention has been given to the resilience, or the ability of systems to "bounce back," of these events. Discussed here is a modeling paradigm for quantifying system resilience, primarily as a function of vulnerability (the adverse initial system impact of the disruption) and recoverability (the speed of system recovery). To account for uncertainty, stochastic measures of resilience are introduced, including Time to Total System Restoration, Time to Full System Service Resilience, and Time to α%-Resilience. These metrics are applied to quantify the resilience of inland waterway ports, important hubs in the flow of commodities, and the port resilience approach is deployed in a data-driven case study for the inland Port of Catoosa in Oklahoma. The contributions herein demonstrate a starting point in the development of a resilience decision making framework. © 2014 Elsevier Ltd. All rights reserved. |
| spellingShingle | Pant, R Barker, K Ramirez-Marquez, J Rocco, C Stochastic measures of resilience and their application to container terminals |
| title | Stochastic measures of resilience and their application to container terminals |
| title_full | Stochastic measures of resilience and their application to container terminals |
| title_fullStr | Stochastic measures of resilience and their application to container terminals |
| title_full_unstemmed | Stochastic measures of resilience and their application to container terminals |
| title_short | Stochastic measures of resilience and their application to container terminals |
| title_sort | stochastic measures of resilience and their application to container terminals |
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