A quantitative approach for assessment and improvement of network resilience

Ahmadian, Navid; Lim, Gino J.; Cho, Jae-Young; Bora, Selim

doi:10.1016/j.ress.2020.106977

Cited by 60 publications

(22 citation statements)

References 32 publications

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“…In terms of research methods, static indicators of network topology in complex network theory have mostly been used. These are based on statistical or survey data [16,44]. The objects of such research include regions at small and medium-sized scales, such as cities and urban agglomerations [21,45].…”

Section: Literature Reviewmentioning

confidence: 99%

Resilience of Urban Network Structure in China: The Perspective of Disruption

Wei

Pan

2021

IJGI

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In light of the long-term pressure and short-term impact of economic and technological globalization, regional and urban resilience has become an important issue in research. As a new organizational form of regional urban systems, the resilience of urban networks generated by flow space has emerged as a popular subject of research. By gathering 2017 data from the Baidu search index, the Tencent location service, and social statistics, this study constructs information, transportation, and economic networks among 344 cities in China to analyze the spatial patterns of urban networks and explore their structural characteristics from the perspectives of hierarchy and assortativity. Transmissibility and diversity were used to represent the resilience of the network structure in interruption scenarios (node failure and maximum load attack). The results show the following: The information, transportation, and economic networks of cities at the prefecture level and higher in China exhibit a dense pattern of spatial distribution in the east and a sparse pattern in the west; however, there are significant differences in terms of hierarchy and assortativity. The order of resilience of network transmissibility and diversity from strong to weak was information, economic, transportation. Transmissibility and diversity had nearly identical scores in response to the interruption of urban nodes. Moreover, a highly heterogeneous network was more likely to cause shocks to the network structure, owing to its cross-regional urban links in case of disturbance. We identified 12 dominant nodes and 93 vulnerable nodes that can help accurately determine the impetus behind network structure resilience. The capacity of regions for resistance and recovery can be improved by strengthening the construction of emergency systems and risk prevention mechanisms.

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Section: Literature Reviewmentioning

confidence: 99%

Resilience of Urban Network Structure in China: The Perspective of Disruption

Wei

Pan

2021

IJGI

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“…There is almost no universally accepted definition of resilience in network. However, the literature agrees on several key aspects of network resilience evaluation, which involves the probability of disruption, the impacts of those disruptions, and the behavior of recovering from the disruption to the normal state [12] [13]. In this paper, we enrich and complement the definition of resilience in network based on NAS's version [5].…”

Section: A Network Resilience Definitionmentioning

confidence: 95%

“…As shown in Fig. 1, the area between resilience curve and dotted P nor , also called resilience triangle [15], is the total loss as a consequence of the disruption.…”

Section: B Time-varying Process Of Network Resiliencementioning

confidence: 99%

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A Quantitative Framework for Network Resilience Evaluation using Dynamic Bayesian Network

Jiang¹,

Yang²,

Cheng³

et al. 2021

Preprint

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Measuring and evaluating network resilience has become an important aspect since the network is vulnerable to both uncertain disturbances and malicious attacks. Networked systems are often composed of many dynamic components and change over time, which makes it difficult for existing methods to access the changeable situation of network resilience. This paper establishes a novel quantitative framework for evaluating network resilience using the Dynamic Bayesian Network. The proposed framework can be used to evaluate the network's multistage resilience processes when suffering various attacks and recoveries. First, we define the dynamic capacities of network components and establish the network's five core resilience capabilities to describe the resilient networking stages including preparation, resistance, adaptation, recovery, and evolution; the five core resilience capabilities consist of rapid response capability, sustained resistance capability, continuous running capability, rapid convergence capability, and dynamic evolution capability. Then, we employ a two-time slices approach based on the Dynamic Bayesian Network to quantify five crucial performances of network resilience based on core capabilities proposed above. The proposed approach can ensure the time continuity of resilience evaluation in time-varying networks. Finally, our proposed evaluation framework is applied to different attacks and recovery conditions in typical simulations and real-world network topology. Results and comparisons with extant studies indicate that the proposed method can achieve more accurate and comprehensive evaluation and can be applied to network scenarios under various attack and recovery intensity.

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“…There are notable works that define and utilize resilience metrics as objectives of their supply chain design. For example, Ahmadian et al defined resilience metrics to analyze the performance of given systems, and developed an optimization model to maximize these metrics toward resilience enhancement under budget limitations 32 . More recently, Yu and Baroud proposed stochastic block models to quantify the uncertain interconnection between interwined networks and studied the effect of single components to the static and dynamic resilience 33 .…”

Section: Introductionmentioning

confidence: 99%

Resilience‐aware design of interconnected supply chain networks with application to water‐energy nexus

Tsolas

Hasan

2021

AIChE Journal

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The effects of natural disasters, pandemic‐induced lockdowns, and other disruptions often cascade across networks. In this work, we use minimum cost of resilience (MCOR) and operation‐based resilience metrics to quantify network performance against single‐connectivity failures and identify critical connections in interconnected networks. MCOR corresponds to the minimum additional infrastructure investment that is required to achieve a certain level of resilience. To guarantee MCOR, we incorporate the metrics in a multi‐scenario mixed‐integer linear program (MILP) that accounts for resilience in the design phase of interconnected networks. The goal is to obtain optimal generation and transportation capacities with flexible operation under all single‐connectivity disruption scenarios. We demonstrate the applicability of our resilience‐aware framework on a water‐energy nexus (WEN) example focusing on grass‐root design and retrofitting. We further apply the framework to analyze a regional WEN and observe that it is possible to achieve “full” resilience in the expense of additional regional investments.

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A quantitative approach for assessment and improvement of network resilience

Cited by 60 publications

References 32 publications

Resilience of Urban Network Structure in China: The Perspective of Disruption

Resilience of Urban Network Structure in China: The Perspective of Disruption

A Quantitative Framework for Network Resilience Evaluation using Dynamic Bayesian Network

Resilience‐aware design of interconnected supply chain networks with application to water‐energy nexus

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