Modeling the recovery process: A key dimension of resilience

Cassottana, Beatrice; Shen, Lijuan; Tang, Loon Ching

doi:10.1016/j.ress.2019.106528

Cited by 51 publications

(49 citation statements)

References 31 publications

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“…In terms of potential future research, TBEA type control charts could be extended, for instance, in order to be able to monitor resilience type data that are known to depend on three characteristics: the time between disruptions (say T 1 ), the performance loss X , and the time needed for recovery (say T 2 ) (see Shen et al 36 and Cassottana et al 37 ). In this context, the idea would be to develop new TBEA type control charts for which the time is no longer a univariate random variable but a bivariate one T = ( T 1 , T 2 ) and the amplitude X remains the same as for traditional TBEA type data.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Shewhart time‐between‐events‐and‐amplitude control charts for correlated data

Rahali

Castagliola

Taleb

et al. 2020

Quality & Reliability Eng

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In recent years, several techniques based on control charts have been developed for the simultaneous monitoring of the time interval T and the amplitude X of events, known as time‐between‐events‐and‐amplitude (TBEA) charts. However, the vast majority of the existing works have some limitations. First, they usually focus on statistics based on the ratio , and second, they only investigate a reduced number of potential distributions, that is, the exponential distribution for T and the normal distribution for X. Moreover, until now, very few research papers have considered the potential dependence between T and X. In this paper, we investigate three different statistics, denoted as Z1, Z2, and Z3, for monitoring TBEA data in the case of three potential distributions (gamma, normal, and Weibull), for both T and X, using copulas as a mechanism to model the dependence. An illustrative example considering times between machine breakdowns and associated maintenance illustrates the use of TBEA control charts.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Shewhart time‐between‐events‐and‐amplitude control charts for correlated data

Rahali

Castagliola

Taleb

et al. 2020

Quality & Reliability Eng

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“…For WDSs subject to individual component failures, i.e., water leakages, relatively slower losses in the demand delivery service followed by sudden recoveries are observed. Other disruptions, such as multiple pipe failures, would result in a sudden loss of performance and could be modeled using the recovery functions developed in Cassottana et al (2019) as discussed in the Introduction. During water leakages, however, WDSs can still meet a certain level of water demand thanks to their distributed water tanks and stand-by pumping capacity (Diao et al 2016).…”

Section: Recovery Function Selectionmentioning

confidence: 99%

“…However, those functions ignored the performance loss process. Therefore, in order to represent both performance loss and recovery, Todman et al (2016) developed a recovery function in analogy with a mechanical spring damper system, while Cassottana et al (2019) proposed families of recovery functions with parameters capable of representing key characteristics of recovery processes. Although these models have proven useful for assessing and comparing resilience, they could only model fast performance losses followed by relatively slower recoveries.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Assessment of System Response during Disruptions: An Application to Water Distribution Systems

Cassottana¹,

Aydin

Tang

2021

J. Water Resour. Plann. Manage.

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The resilience of water distribution systems (WDSs) has gained increasing attention in recent years. Various performance loss and recovery behaviors have been observed for WDSs subject to disruptions. However, a model for their characterization, which could provide further insight for resilience assessment and enhancement, is still lacking. Here, the authors develop a recovery function to model WDS performance over time following a disruption. This function is useful to compare system responses under different disruption and recovery scenarios and supports the identification of areas for improvement within various aspects of the resilience of a WDS. The proposed model was applied to two benchmark networks. Different scenarios were analyzed in which one node at a time was disrupted and two recovery strategies were implemented. It was found that the developed model supports the implementation of tailored strategies to improve WDS resilience according to the location of the disruption, therefore enhancing the efficient allocation of resources.

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“…

E_{S} = \frac{d G false(t false)}{d t}, t_{0} \leq t \leq t_{r} .

Recovery paths could be measured by recovery functions. Different types of recovery functions have been developed, such as exponential functions, power functions, hybrid family, gamma family, and so on. The recovery paths of these functions are shown in Figure . Reconfigurability.…”

Section: Key Dimensions Of Resiliencementioning

confidence: 99%

“…A, Exponential recovery functions, B, power recovery functions, C, hybrid recovery functions, and D, gamma recovery functions, where

a, b, c,

and

d

are parameters of recovery functions…”

Section: Key Dimensions Of Resiliencementioning

confidence: 99%

Large‐scale systems resilience: A survey and unifying framework

Shen¹,

Cassottana

Heinimann³

et al. 2020

Quality & Reliability Eng

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A unifying framework in defining and measuring resilience has been an intense research topic. In this paper, resilience is measured as a function of intrinsic capacities of a system, the effectiveness of recovery, and the extrinsic random shock process. Some existing resilience measures are analyzed as special cases of the proposed unifying measure. Then, we develop a framework in which the key constituents in achieving resilience are identified. Resilience is represented by four key dimensions: reliability, robustness, recovery, and reconfigurability. Finally, some practical and specific strategies are proposed to enhance the resilience of critical infrastructure systems under the proposed framework.

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Modeling the recovery process: A key dimension of resilience

Cited by 51 publications

References 31 publications

Evaluation of Shewhart time‐between‐events‐and‐amplitude control charts for correlated data

Evaluation of Shewhart time‐between‐events‐and‐amplitude control charts for correlated data

Quantitative Assessment of System Response during Disruptions: An Application to Water Distribution Systems

Large‐scale systems resilience: A survey and unifying framework

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