Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management

Nozhati, Saeed; Sarkale, Yugandhar; Ellingwood, Bruce R.; Chong, Edwin K. P.; Mahmoud, Hussam

doi:10.1016/j.ress.2018.09.011

Cited by 50 publications

(27 citation statements)

References 38 publications

(50 reference statements)

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“…Moreover, a sequential discrete optimization approach was proposed as a decision-making framework at the community level for post-hazard recovery. The proposed methodology overcomes the limitations of dimensionality and manages large-scale infrastructure systems following disasters to enhance community disaster resilience [ 115 ].…”

Section: The Three Phases Of Urban Disaster Resilience Researchmentioning

confidence: 99%

A Scientometric Review of Urban Disaster Resilience Research

Yang

Tan

et al. 2021

IJERPH

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Natural disasters and human-made disasters are threatening urban areas globally. The resilience capacity of the urban system plays an important role in disaster risk response and recovery. Strengthening urban disaster resilience is also fundamental to ensuring sustainable development. Various practices and research for enhancing urban disaster resilience have been carried out worldwide but are yet to be reviewed. Accordingly, this paper gives a scientometric review of urban disaster resilience research by using CiteSpace. The time span (January 2001–January 2021) was selected and divided into three phases based on the number of publications. In addition, according to keyword statistics and clustering results, the collected articles are grouped into four hotspot topics: disaster risk reduction, specific disaster resilience research, resilience assessment, and combination research. The results show that most of the existing research is in the first two categories, and articles in the second and fourth categories both show a high growth rate and could be further research directions. The review indicates that urban disaster resilience is essential for a city’s sustainable development. Moreover, the findings provide scholars a full picture of the existing urban disaster resilience research which can help them identify promising research directions. The findings can also help urban government officials and policymakers review current urban disaster management strategies and make further improvements.

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Section: The Three Phases Of Urban Disaster Resilience Researchmentioning

confidence: 99%

A Scientometric Review of Urban Disaster Resilience Research

Yang

Tan

et al. 2021

IJERPH

View full text Add to dashboard Cite

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“…Hence, we consider food retailers and their dependencies to EPN and WN during restoration analysis and optimization. To capture the effects of the restoration of each food retailer on different households over the community, we use a gravity model [19,20], which assigns the shopping probabilities based on the food retailers' capacities and locations so that bigger and closer retailers have greater impacts on household units.…”

Section: Food Retailersmentioning

confidence: 99%

Optimal stochastic dynamic scheduling for managing community recovery from natural hazards

Nozhati

Sarkale

Chong

et al. 2020

Reliability Engineering & System Safety

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Following the occurrence of an extreme natural or man-made event, community recovery management should aim at providing optimal restoration policies for a community over a planning horizon. Calculating such optimal restoration polices in the presence of uncertainty poses significant challenges for community leaders. Stochastic scheduling for several interdependent infrastructure systems is a difficult control problem with huge decision spaces. The Markov decision process (MDP)-based optimization approach proposed in this study incorporates different sources of uncertainties to compute the restoration policies. The computation of optimal scheduling schemes using our method employs the rollout algorithm, which provides an effective computational tool for optimization problems dealing with real-world large-scale networks and communities. We apply the proposed methodology to a realistic community recovery problem, where different decision-making objectives are considered. Our approach accommodates current restoration strategies employed in recovery management. Our computational results indicate that the restoration policies calculated using our techniques significantly outperform the current recovery strategies. Finally, we study the applicability of our method to address different risk attitudes of policymakers, which include risk-neutral and risk-averse attitudes in the community recovery management. become complicated. The most important characteristics of a rational decision-making approach include:i. The agent must balance the reluctance for low immediate reward with the desire of high future rewards (also referred as "non-myopic agent" or look-ahead property); ii.The agent must consider different sources of uncertainties; iii. The agent must make decisions periodically to not only take advantage of information that becomes available when recovery actions are in progress but also to adapt to disturbances over the recovery process; iv.The agent must be able to handle a large decision-making space, which is typical for the problems at the community level. This decision-making space can cause an agent to suffer from decision fatigue; no matter how rational and high-minded an agent tries to be, one cannot make decision after decision without paying a cost [1]. v.The agent must consider different types of dependencies and interdependencies among networks, because a single decision can trigger cascading effects in multiple networks at the community level. vi.The agent must be able to handle multi-objective tasks, which are common in real-world domains. The interconnectedness among networks and probable conflicts among competing objectives complicate the decision-making procedure. vii.The agent must consider different constraints, such as time constraints, limited budget and repair crew, and current regional entities' policies. viii.External factors, like the available resources and the type of community and hazard, shape the risk attitude of the agent. The different risk behaviors must be considered.Community-level de...

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“…Other approaches define the recovery planning as a sequential decision problem, which is optimized in a dynamic programming approach (Nozhati et al 2019;Faturechi et al 2014).…”

Section: Resilience Policiesmentioning

confidence: 99%

Critical Airport Infrastructure Disaster Resilience: A Framework and Simulation Model for Rapid Adaptation

2020

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Resilient critical airport infrastructures affected by a disaster need to sustain minimal functionality and quickly resume full operation, while at the same time coping with the increased operational demands imposed by the unfolding disaster response. In this paper, we develop a resilience framework and model-driven approach that focuses on the ability of the infrastructure to rapidly adapt to a new steady state under these conditions. This requires both the extension of capacity as well as the adaptation of key processes. Through discrete event simulations, we study the implications of different policies to improve airport resilience under different disaster impact scenarios for a stylized case. Our results show that although decision-makers may be tempted to focus on short-term measures that can be implemented immediately, resilience is improved most by a combination of rapid process changes and longer-term measures that structurally increase airport capacity.

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Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management

Cited by 50 publications

References 38 publications

A Scientometric Review of Urban Disaster Resilience Research

A Scientometric Review of Urban Disaster Resilience Research

Optimal stochastic dynamic scheduling for managing community recovery from natural hazards

Critical Airport Infrastructure Disaster Resilience: A Framework and Simulation Model for Rapid Adaptation

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