Defining resilience using probabilistic event trees

Teodorescu, Horia-Nicolai

doi:10.1007/s10669-015-9550-9

Cited by 14 publications

(9 citation statements)

References 36 publications

(47 reference statements)

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“…Ettouney () addressed this by stating: “Even though Recovery can be considered to be strongly related to consequence…it has limited relationship with the vulnerability of the system.” The literature also uses the idea of a recovery level to estimate resilience. Teodorescu () proposed four steps to estimate resilience: (1) determine reasonable recovery level, (2) determine time horizon for recovery at a determined level, (3) determine all reasonable threats, and (4) determine what part of the equipment, facilities, and human resources will recover. Jackson () identified total recovery, partial recovery, and total decommissioning as recovery levels.…”

Section: Mission Resilience Quantificationmentioning

confidence: 99%

Assessing Engineering Resilience for Systems with Multiple Performance Measures

et al. 2019

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Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other "ilities" directly in the value hierarchy, while the second method quantifies mission resilience and uses the "ilities" in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems-of-systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.

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Section: Mission Resilience Quantificationmentioning

confidence: 99%

Assessing Engineering Resilience for Systems with Multiple Performance Measures

et al. 2019

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“…Barker et al [97], Shafieezadeh and Ivey Burden [98], and Kong and Simonovic [99] defined resilience as the "proportion of affected performance of the system after disruption to the normal performance of the system without disruption". Teodorescu [100] defined resilience as the "ability of a system to recover its normal performance level". He believed resilience is a function of the recovery process duration and the system performance level after the recovery process.…”

Section: How Is the Term "Resilience" Defined In The Discipline Of Technological Ciss?mentioning

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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“…Many definitions exist, and many reviews have been undertaken to attempt to clarify these definitions (see for example Zhou et al 2008;Haimes 2009;Rose 2009;Aven 2011;Bhamra et al 2011;Alexander 2013;Francis and Bekera 2014;Baum 2015;Bergström et al 2015;Righi et al 2015;Teodorescu 2015;Woods 2015;Cimellaro et al 2016;Hosseini et al 2016;Ibanez et al 2016;Connelly et al 2017;Kurth et al 2018;Patriarca et al 2018;Xue et al 2018). The many distinctions and interpretations are dependent on which aspect of a resilient system is under scrutiny (Sikula et al 2015;Lofquist 2017).…”

Section: Resilience and Resilience Engineering In Infrastructurementioning

confidence: 99%

Resilience engineering: theory and practice in interdependent infrastructure systems

et al. 2018

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The economy and well-being of modern societies relies on complex and interdependent infrastructure systems to enable delivery of utilities and movement of goods, people and services. This complexity has resulted in an increased potential for cascading failures, whereby small scale initial failures in one system can result in events of catastrophic proportions across the wider network. Resilience and the emerging concept of resilience engineering within infrastructure are among the main concerns of those managing such complex systems. However, the disparate nature of resilience engineering development in various academic and industrial regimes has resulted in a diversity of definitions and characterisations. These are discussed in this paper, as are the commonalities between sectors and between different engineering disciplines. The paper also highlights the various methodologies used as part of resilience engineering implementation and monitoring, current practices including existing approaches and metrics, and an insight into the opportunities and potential barriers associated with these methodologies and practices. This research was undertaken for the Resilience Shift initiative to shift the approach to resilience in practice for critical infrastructure sectors. The programme aims to help practitioners involved in critical infrastructure to make decisions differently, contributing to a safer and better world.

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Defining resilience using probabilistic event trees

Cited by 14 publications

References 36 publications

Assessing Engineering Resilience for Systems with Multiple Performance Measures

Assessing Engineering Resilience for Systems with Multiple Performance Measures

The Resilience of Critical Infrastructure Systems: A Systematic Literature Review

Resilience engineering: theory and practice in interdependent infrastructure systems

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