A metric and frameworks for resilience analysis of engineered and infrastructure systems

Francis, Royce A.; Bekera, Behailu B.

doi:10.1016/j.ress.2013.07.004

Cited by 868 publications

(493 citation statements)

References 43 publications

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“…Based on the resilience developments for complex socio-technical systems in other domains, we may identify four key directions for addressing resilience in air transportation. The first is the elaboration of the unifying resilience framework of Francis and Bekera (2014) for the air transportation domain -one of the challenges here is to incorporate the various stakeholders into a unifying framework, with clear links to the SESAR objectives of collaborative decision making (CDM). The second is the further investigation and incorporation of dedicated resilience metrics in air transportation, as discussed in Gluschenko and Foerster (2013).…”

Section: Discussionmentioning

confidence: 99%

“…Based on a review of the complementary resilience developments in the various domains, Francis and Bekera (2014) identified the following three key capacities of resilience: (i) absorptive capacity, (ii) recoverability, and (iii) adaptive capacity. These key capacities have been integrated into a unifying resilience framework for complex socio-technical systems (ibid.).…”

Section: Defining and Modelling Resiliencementioning

confidence: 99%

“…Gunderson et al (2002) introduced two such key metrics: (1) ecological resilience is the "amount of disturbance that a system can absorb before it changes"; (2) engineering resilience is the "time of return to a global equilibrium following a disturbance". In the study by Francis and Bekera (2014), the quantitative metric (3) 'resilience factor' has been proposed in order to take account of all three key resilience capacities.…”

Section: Defining and Modelling Resiliencementioning

confidence: 99%

See 2 more Smart Citations

Applying complexity science to air traffic management

Cook

Blom

Lillo

et al. 2015

Journal of Air Transport Management

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Complexity science is the multidisciplinary study of complex systems. Its marked network orientation lends itself well to transport contexts. Key features of complexity science are introduced and defined, with a specific focus on the application to air traffic management. An overview of complex network theory is presented, with examples of its corresponding metrics and multiple scales. Complexity science is starting to make important contributions to performance assessment and system design: selected, applied air traffic management case studies are explored. The important contexts of uncertainty, resilience and emergent behaviour are discussed, with future research priorities summarised.

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Section: Discussionmentioning

confidence: 99%

Section: Defining and Modelling Resiliencementioning

confidence: 99%

Section: Defining and Modelling Resiliencementioning

confidence: 99%

See 1 more Smart Citation

Applying complexity science to air traffic management

Cook

Blom

Lillo

et al. 2015

Journal of Air Transport Management

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show abstract

“…This is termed the restorative capacity of the system. These ideas are implicitly laid out in Francis andBekera, 2014, andexplicitly in Rose, 2004a. In the disaster literature, the concept of resilience appears to stem from three separate concepts: vulnerability, risk assessment, and adaptive capacity. All of these ideas are linked to a common goal of reducing a community's risk to external forces (Lei et al, 2014).…”

Section: Foundations Of Resiliencementioning

confidence: 99%

Landscape Survey to Support Flood Apex National Flood Decision Support Toolbox: Definitions and Existing Tools

Strong¹,

Knopman²

2017

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“…First, the design principles described here are based upon static rather than dynamic measures of resilience. Although many authors have identified the need for such dynamic measures, the literature has yet to produce them [7,9,10,32,45,64,76,90,95]. Therefore, it is likely that the design principles described here will be expanded as the system resilience literature develops further.…”

Section: Design Principles For a Change Of System Dynamicsmentioning

confidence: 99%

Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems

Farid

2015

Intell Ind Syst

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Recently, the academic and industrial literature has coalesced around an enhanced vision of the electric power grid that is intelligent, responsive, dynamic, adaptive and flexible. One particularly emphasized "smart-grid" property is that of resilience where healthy regions of the grid continue to operate while disrupted and perturbed regions bring themselves back to normal operation. Multi-agent systems have recently been proposed as a key enabling technology for such a resilient control scheme. While the power system literature has often addressed multi-agent systems, many of these works did not have resilience as the central design intention. This paper now has a two-fold purpose. First, it seeks to identify a set of multi-agent system design principles for resilient coordination and control of future power systems. To that end, it draws upon an axiomatic design for large flexible engineering systems model which was recently used in the development of resilience measures. From this quantitative model, a set of design principles are easily distilled. Second, the paper assesses the adherence of existing multi-agent system implementations with respect to these design principles. The paper concludes that while many multi-agent systems have been developed for power grids, they have been primarily intended as the decentralization of a particular decision-making/control algorithm. Thus many of the works make only limited contributions to power grid resilience.B Amro M. Farid

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A metric and frameworks for resilience analysis of engineered and infrastructure systems

Cited by 868 publications

References 43 publications

Applying complexity science to air traffic management

Applying complexity science to air traffic management

Landscape Survey to Support Flood Apex National Flood Decision Support Toolbox: Definitions and Existing Tools

Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems

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