A post-disaster resource allocation framework for improving resilience of interdependent infrastructure networks

Sun, Jingran; Zhang, Zhanmin

doi:10.1016/j.trd.2020.102455

Cited by 66 publications

(14 citation statements)

References 22 publications

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“…For this reason, the authors proposed a solution that offers a practical tool for decision-makers to distinguish between S/Ss and their mutual associations, which enables them to identify the critical S/Ss. In study [45], researchers aimed to find the critical infrastructures and the risk associated with compound flooding causes.…”

Section: Systematic Literature Surveymentioning

confidence: 99%

“…Comparing risks across hazards on a common and consistent basis, such as the value of property damage, allows identifying which hazards are associated with higher expected losses. One example of a multi-hazard study in damage to buildings that quantifies the damage resulting from a range of natural hazards can be found in [45]. As described, this step not only considers analytics for decision making but also includes the possibility to develop predictions of security risks and incidents, including the assessment of their implications on smart systems and infrastructure and cascading effects internal to the SCCI.…”

Section: The Implementation Of Scforcesmentioning

confidence: 99%

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Disaster Management in Smart Cities

et al. 2021

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The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network of critical infrastructures. This paper proposes an integrated resilience system linking interconnected critical infrastructures in a smart city to improve disaster resilience. A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level). The proposed approach allows rapid recovery of infrastructures’ service performance levels after disasters while keeping the coverage of the assessment of risks, prevention, detection, response, and mitigation of consequences. The proposed approach has the originality and the practical implication of providing a decision support system that handles the infrastructures that will support the city disaster management system—make the city prepare, adapt, absorb, respond, and recover from disasters by taking advantage of the interconnections between its various critical infrastructures to increase the overall resilience capacity. The city of Lisbon (Portugal) is used as a case to show the practical application of the approach.

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Section: Systematic Literature Surveymentioning

confidence: 99%

Section: The Implementation Of Scforcesmentioning

confidence: 99%

Disaster Management in Smart Cities

et al. 2021

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“…As discussed earlier, agent-based models essentially consist of a set of interacting agents, and a set of rules to define their independent behaviors. Previous studies have utilized ABM for modeling infrastructure interdependencies and optimizing the resource allocation strategies during the restoration phase after an extreme event (Pumpuni-Lenss et al, 2017;Sun and Zhang, 2020).…”

Section: Agent-based Approach For Modeling Interdependenciesmentioning

confidence: 99%

“…There are studies related to allocate resources for infrastructure resilience optimization with the consideration of the infrastructure interdependencies (Kong et al, 2019;Pumpuni-Lenss et al, 2017;Sun and Zhang, 2020;Zhang et al, 2018). All these studies focus on allocating resources after an extreme event to minimize the restoration cost or expedite the recovery speed.…”

Section: Common Infrastructure Management Practicesmentioning

confidence: 99%

A resource allocation framework for predisaster resilience management of interdependent infrastructure networks

Sun

Balakrishnan

Zhang

2020

BEPAM

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PurposeResource allocation is essential to infrastructure management. The purpose of this study is to develop a methodological framework for resource allocation that takes interdependencies among infrastructure systems into consideration to minimize the overall impact of infrastructure network disruptions due to extreme events.Design/methodology/approachTaking advantage of agent-based modeling techniques, the proposed methodology estimates the interdependent effects of a given infrastructure failure which are then used to optimize resource allocation such that the network-level resilience is maximized.FindingsThe findings of the study show that allocating resources with the proposed methodology, where optimal infrastructure reinforcement interventions are implemented, can improve the resilience of infrastructure networks with respect to both direct and interdependent risks of extreme events. These findings are also verified by the results of two case studies.Practical implicationsAs the two case studies have shown, the proposed methodological framework can be applied to the resource allocation process in asset management practices.Social implicationsThe proposed methodology improves the resilience of the infrastructure network, which can alleviate the social and economic impact of extreme events on communities.Originality/valueCapitalizing on the combination of agent-based modeling and simulation-based optimization techniques, this study fulfills a critical gap in infrastructure asset management by incorporating infrastructure interdependence and resilience concepts into the resource allocation process.

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“…Similar studies combine simulation and linear programming optimization to simulate disaster-related infrastructure damage and the resulting optimal repair crew assignments (Brown and Vassiliou 1993). More recently, agent-based modeling has been employed to simulate how these 205 repair crews make decisions while responding to an event (Sun and Zhanmin 2020).…”

mentioning

confidence: 99%

A simulation-optimization framework for post-disaster allocation of mental health resources

Cunningham

Schuldt

Chini

et al. 2021

Preprint

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Abstract. Extreme events, such as natural or human-caused disasters, cause mental health stress in affected communities. While the severity of these outcomes varies based on socioeconomic standing, age group, and degree of exposure, disaster planners can mitigate potential stress-induced mental health outcomes by assessing the capacity and scalability of early, intermediate, and long-term treatment interventions by social workers and psychologists. However, local and state authorities are typically underfunded, understaffed, and have ongoing health and social service obligations that constrain mitigation and response activities. In this research, a resource assignment framework is developed as a coupled-state transition and linear optimization model that assists planners in optimally allocating constrained resources and satisfying mental health recovery priorities post-disaster. The resource assignment framework integrates the impact of a simulated disaster on mental health, mental health provider capacities, and the Center for Disease Control and Prevention's (CDC) Social Vulnerability Index (SVI) to identify vulnerable populations needing additional assistance post-disaster. In this study, we optimally distribute mental health clinicians to treat the affected population based upon rulesets that simulate decision-maker priorities, such as economic and social vulnerability criteria. Finally, the resource assignment framework maps the mental health recovery of the disaster-affected populations over time, providing agencies a means to prepare for and respond to future disasters given existing resource constraints. These capabilities hold the potential to support decision-makers in minimizing long-term mental health impacts of disasters on communities through improved preparation and response activities.

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A post-disaster resource allocation framework for improving resilience of interdependent infrastructure networks

Cited by 66 publications

References 22 publications

Disaster Management in Smart Cities

Disaster Management in Smart Cities

A resource allocation framework for predisaster resilience management of interdependent infrastructure networks

A simulation-optimization framework for post-disaster allocation of mental health resources

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