Design and Assessment Methodology for System Resilience Metrics

Najarian, Mohammad; Lim, Gino J.

doi:10.1111/risa.13274

Cited by 37 publications

(29 citation statements)

References 58 publications

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“…Besides, we also refer to the general discussion reported in [46]. Outside the ICT domain, other works that have presented clarifying models, conceptual frameworks and possible metrics for resilience assessment (and that have inspired some of our contributions) are, for example, [12,19,29,35]. We refer to (and briefly comment) still other works in the next sections of this paper.…”

Section: Problem Gap and Objectives For A Solutionmentioning

confidence: 92%

“…Broadly speaking, this property concerns the overall accumulated "quality" (or "degradation") of the service delivered by the system. Referring to the resilience literature, this property is referred to as reduced consequences from failures in [12], "static" resilience in [35], level of recovery in [19], absorption and adaptation in [29].…”

Section: Metrics For Quantifying the Consequences From Failurementioning

confidence: 99%

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A conceptual framework for resilience: fundamental definitions, strategies and metrics

et al. 2020

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The resilience system property has become more and more relevant, mainly because of the increasing dependance on a rapidly growing number of software-intensive, complex, socio-technical systems, which are facing uncertainty about changes they are expected to experience during their life-cycle and ways to deal with them. Methodologies for the systematic design and validation of resilience for such systems are thus highly necessary, and require contributions from several different fields. This paper contributes to current resilience research by providing a conceptual framework intended to serve as a common ground for the development of such methodologies. Its main points are: the identification of the main categories of changes a system should face; a clear definition of the different facets of resilience one could want to achieve, expressed in terms of the system dynamics; a mapping of each of these facets to design strategies that are better suited to achieve it; and the corresponding identification of possible metrics that can be used to assess its achievement.

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Section: Problem Gap and Objectives For A Solutionmentioning

confidence: 92%

Section: Metrics For Quantifying the Consequences From Failurementioning

confidence: 99%

A conceptual framework for resilience: fundamental definitions, strategies and metrics

et al. 2020

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“…Robustness is measured by the CIS's amount of residual performance after a disruption [75,115]. Furthermore, survivability, resistant ability, and stability have a similar definition to robustness [75,126,129,130].…”

Section: What Types Of Terms Define/determine the Resilience Of Technological Ciss?mentioning

confidence: 99%

“…Some pre-disruption (preparedness) and post-disruption (recovery) activities need to be established to achieve these abilities. Pre-disruption activities target the CIS's ability to protect, resist, absorb, adapt, and recover, and post-disruption activities target the CIS's ability to adapt and recover [7,117,120,[123][124][125][126]. According to Faturechi et al [123], investment costs on the CIS's preparedness are mandatory, whether a disruption occurs or not.…”

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confidence: 99%

The Resilience of Critical Infrastructure Systems: A Systematic Literature Review

et al. 2021

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Risk management is a fundamental approach to improving critical infrastructure systems’ safety against disruptive events. This approach focuses on designing robust critical infrastructure systems (CISs) that could resist disruptive events by minimizing the possible events’ probability and consequences using preventive and protective programs. However, recent disasters like COVID-19 have shown that most CISs cannot stand against all potential disruptions. Recently there is a transition from robust design to resilience design of CISs, increasing the focus on preparedness, response, and recovery. Resilient CISs withstand most of the internal and external shocks, and if they fail, they can bounce back to the operational phase as soon as possible using minimum resources. Moreover, in resilient CISs, early warning enables managers to get timely information about the proximity and development of distributions. An understanding of the concept of resilience, its influential factors, and available evaluation and analyzing tools are required to have effective resilience management. Moreover, it is important to highlight the current gaps. Technological resilience is a new concept associated with some ambiguity around its definition, its terms, and its applications. Hence, using the concept of resilience without understanding these variations may lead to ineffective pre- and post-disruption planning. A well-established systematic literature review can provide a deep understanding regarding the concept of resilience, its limitation, and applications. The aim of this paper is to conduct a systematic literature review to study the current research around technological CISs’ resilience. In the review, 192 primary studies published between 2003 and 2020 are reviewed. Based on the results, the concept of resilience has gradually found its place among researchers since 2003, and the number of related studies has grown significantly. It emerges from the review that a CIS can be considered as resilient if it has (i) the ability to imagine what to expect, (ii) the ability to protect and resist a disruption, (iii) the ability to absorb the adverse effects of disruption, (iv) the ability to adapt to new conditions and changes caused by disruption, and (v) the ability to recover the CIS’s normal performance level after a disruption. It was shown that robustness is the most frequent resilience contributing factor among the reviewed primary studies. Resilience analysis approaches can be classified into four main groups: empirical, simulation, index-based, and qualitative approaches. Simulation approaches, as dominant models, mostly study real case studies, while empirical methods, specifically those that are deterministic, are built based on many assumptions that are difficult to justify in many cases.

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“…It defined resilience as “ the ability of a CI system exposed to hazards to resist, absorb, accommodate to and recover from the effects of a hazard in a timely and efficient manner, for the preservation and restoration of essential services ” (Petersen et al 2020 ). To date, the resilience concept has been adopted in various disciplines, such as engineering (Rød et al 2016 ; Najarian and Lim 2019 ), social (Amico and Currà 2014 ; Zhang and Huang 2018 ), economic (Vugrin et al 2011 ; Pant et al 2014 ), ecological (Holling 1973 ; Müller et al 2016 ), socio-technical (Omer et al 2014 ; Cook et al 2016 ), and socio-ecological systems (Brown and Williams 2015 ; Meng et al 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Resilience analysis: A formulation to model risk factors on complex system resilience

Mottahedi

Sereshki

Ataei

et al. 2021

Int J Syst Assur Eng Manag

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Resilience is about the ability of the system to resist, adapt to, and expeditiously recover from a disruptive event. The first and maybe the crucial step of resilience management is known as resilience analysis. However, there are many obstacles in front of the analyzers to analyze the resilience of systems. One of these obstacles is precise resilience data accessibility. Conventional resilience analysis methods frequently only consider historical data (e.g., time to repair and time to failure). However, to analyze the system resilience more precisely, the effect of the risk factors, which are known as observed and unobserved covariates, should be considered in the collected resilience database. These covariates will lead to the observed and unobserved heterogeneities among the collected database. Ignoring the effect of covariate may lead to erroneous conclusion about the resilience level of the system. Since it is hard to find a homogeneous operating condition, in this study, a formulation is proposed to model the effect of these covariates on complex system resilience. Finally, it is applied to a transportation system of a surface mine.

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Design and Assessment Methodology for System Resilience Metrics

Cited by 37 publications

References 58 publications

A conceptual framework for resilience: fundamental definitions, strategies and metrics

A conceptual framework for resilience: fundamental definitions, strategies and metrics

The Resilience of Critical Infrastructure Systems: A Systematic Literature Review

Resilience analysis: A formulation to model risk factors on complex system resilience

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