Stochastic measures of resilience and their application to container terminals

Pant, Raghav; Barker, Kash; Ramírez-Márquez, José Emmanuel; Rocco, Claudio M.

doi:10.1016/j.cie.2014.01.017

Cited by 168 publications

(75 citation statements)

References 18 publications

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“…However, the most common resilience analyses deal with descriptive and qualitative analysis. It is then still challenging to define consensual metrics for quantitative resilience analysis (Hollnagel et al 2008;Johnston et al 2008;Hollnagel et al 2011;Stewart & Yuen 2011;Barker et al 2012;Dinh et al 2012;Miller-Hooks et al 2012;Shirali et al 2012;Francis & Bekera 2014;Manyena 2014;Matthews et al 2014;Pant et al 2014;Roege et al 2014;Shafieezadeh & Burden 2014;Aldunce et al 2015;Angeon & Bates 2015;Bond et al 2015;Cardoso et al 2015;Chopra & Khanna 2015;Dijkstra & Viebahn 2015;Kelman et al 2015;Khalili et al 2015;Labaka et al 2015;Lindbom et al 2015;Lundberg & Johansson 2015;Mugume et al 2015;Oken et (Mebarki et al 2014a(Mebarki et al , 2014bMebarki and Barroca 2015). (a) General framework and (b) utility functions and post-disaster recovery.…”

Section: Resilience and Metricsmentioning

confidence: 99%

Natural hazards, vulnerability and structural resilience: tsunamis and industrial tanks

Mébarki

Jeréz

Prod'Homme

et al. 2016

Geomatics, Natural Hazards and Risk

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The paper presents an integrated framework which deals with natural hazards (tsunamis), physical vulnerability modelling, risk of failure for industrial structures (metal structures) and structural resilience provided by plastic adaptation. Simplified models are proposed to describe the runup and wave height attenuation in case of tsunamis. The results are calibrated in the case of important tsunamis having taken place in Asian region. The mechanical vulnerability of cylindrical metal tanks erected near the shoreline is also investigated. The fragility curves are then developed in order to describe the multimodal failure: overturning, rupture of anchorages and sliding, buoyancy, excessive bending effects or buckling. Corresponding fragility curves are developed under various conditions: height of tsunami waves, filling ratios and service conditions of the tanks, friction tank/ground as well as dimensions effects. Probabilistic description of the natural hazard and the fragility curves are presented. Sensitivity analysis is also performed in order to investigate the effect of various governing parameters. Furthermore, resilience concepts and metrics are proposed. Theoretical description of the damages and postdisaster recovery functions are discussed: plastic adaptation as well as elastic and plastic attractors.

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Section: Resilience and Metricsmentioning

confidence: 99%

Natural hazards, vulnerability and structural resilience: tsunamis and industrial tanks

Mébarki

Jeréz

Prod'Homme

et al. 2016

Geomatics, Natural Hazards and Risk

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“…En otra línea de medición de resiliencia se definen los estados del sistema antes, durante y después de un evento disruptivo, con el fin elaborar un perfil más amplio de un sistema con una modelación del proceso de interrupción y recuperación ajustados en una mayor proporción a la realidad [27][28][29][30][31].…”

Section: X: Pérdida Del Nivel De Desempeño T: Tiempo De Recuperación unclassified

Modelo de aproximación lineal para la medición de resiliencia en cadenas de suministro

Rodríguez

Rueda

Guerrero

et al. 2017

Ingeniare. Rev. chil. ing.

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RESUMENLa identificación del nivel de resiliencia de un sistema es fundamental para la toma de decisiones en la prevención y planeación de estrategias de recuperación ante fallas que lo puedan afectar. Este artículo busca diseñar una métrica de aproximación lineal para la medición de resiliencia en cadenas de suministro ante eventos disruptivos inesperados. Las métricas tradicionales de resiliencia simplifican el comportamiento de un sistema después de la ocurrencia de una falla, dificultando que las mediciones se puedan realizar en escenarios de fallas de mayor complejidad. Una métrica general de resiliencia es desarrollada y validada en un caso simulado de una cadena de suministro de dos eslabones con interrupciones en el proceso de transporte. Los resultados confirman que las métricas de resiliencia tradicionales sobreestiman los niveles de resiliencia del sistema, debido a la inhabilidad de modelar diferentes escenarios de eventos disruptivos. Los resultados del caso simulado sugieren que la nueva métrica mejora la estimación de resiliencia en comparación con las métricas lineales previas y, adicionalmente provee la flexibilidad necesaria para ser utilizada en otros tipos de sistema diferentes a cadenas de suministro.Palabras clave: Métricas de resiliencia, cuantificar resiliencia, sistemas resilientes, administración de riesgos en cadenas de suministro, interrupciones en la cadena de suministro. ABSTRACT The following research aims to the design of a linear metric approximation for the measurement of supply chain resilience under unexpected events. The identification of the system's resilience level is a key step for the decision making in the prevention

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“…(18) Such a metric can be used to identify the most important components in a network, such as a transportation or power network, with the goal of prioritizing investments to protect the most influential components. (19) Incorporating uncertainty with this metric produces a stochastic measure of resilience, and the optimal recovery activity can be identified via simulation (20) or by comparing the percentiles of different probability distributions of resilience generated by different recovery activities. (21) Although the motivation behind measuring resilience typically focuses on helping decision makers prepare and respond to disruptions, less research has concentrated on how resources should be allocated to improve an organization or system's resilience.…”

Section: [Insert Fig 1]mentioning

confidence: 99%

“…The literature studying how to optimize system resilience usually defines activities to pursue or components to protect and then uses a numerical method such as simulation to select the best alternative. (19,20,21) In a real-world context, however, it is important to keep in mind that there are limits not only on how quickly a system can recover from a disruption, but also on the physical and financial resources available to help offset the disaster's impacts. A process manager will thus need to focus on determining the most effective way to allocate his or her resources in order to improve system resilience.…”

Section: [Insert Fig 1]mentioning

confidence: 99%

Allocating Resources to Enhance Resilience, with Application to Superstorm Sandy and an Electric Utility

MacKenzie

Zobel

2015

Risk Analysis

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This article constructs a framework to help a decisionmaker allocate resources to increase his or her organization's resilience to a system disruption, where resilience is measured as a function of the average loss per unit time and the time needed to recover full functionality. Enhancing resilience prior to a disruption involves allocating resources from a fixed budget to reduce the value of one or both of these characteristics. We first look at characterizing the optimal resource allocations associated with several standard allocation functions. Because the resources are being allocated before the disruption, however, the initial loss and recovery time may not be known with certainty. We thus also apply the optimal resource allocation model for resilience to three models of uncertain disruptions: (1) independent probabilities, (2) dependent probabilities, and (3) ABSTRACTThis paper constructs a framework to help a decision maker allocate resources to increase his or her organization's resilience to a system disruption, where resilience is measured as a function of the average loss per unit time and the time needed to recover full functionality. Enhancing resilience prior to a disruption involves allocating resources from a fixed budget to reduce the value of one or both of these characteristics. We first look at characterizing the optimal resource allocations associated with several standard allocation functions. Because the resources are being allocated before the disruption, however, the initial loss and recovery time may not be known with certainty. We thus also apply the optimal resource allocation model for resilience to three models of uncertain disruptions: (1) independent probabilities, (2) dependent probabilities, and (3) unknown probabilities. The optimization model is applied to an example of increasing the resilience of an electric power network following Superstorm Sandy.

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Stochastic measures of resilience and their application to container terminals

Cited by 168 publications

References 18 publications

Natural hazards, vulnerability and structural resilience: tsunamis and industrial tanks

Natural hazards, vulnerability and structural resilience: tsunamis and industrial tanks

Modelo de aproximación lineal para la medición de resiliencia en cadenas de suministro

Allocating Resources to Enhance Resilience, with Application to Superstorm Sandy and an Electric Utility

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