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Disaster Analytics for Critical Infrastructures : Methods and Algorithms for Modeling Disasters and Proactive Recovery Preparedness.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Disaster Analytics for Critical Infrastructures : Methods and Algorithms for Modeling Disasters and Proactive Recovery Preparedness./
作者:	Inanlouganji, Alireza.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	126 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Annealing. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28543196
ISBN:	9798535543574

Disaster Analytics for Critical Infrastructures : Methods and Algorithms for Modeling Disasters and Proactive Recovery Preparedness.
Inanlouganji, Alireza.

Disaster Analytics for Critical Infrastructures : Methods and Algorithms for Modeling Disasters and Proactive Recovery Preparedness. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 126 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Arizona State University, 2021.

This item must not be sold to any third party vendors.

Natural disasters are occurring increasingly around the world, causing significant economic losses. To alleviate their adverse effect, it is crucial to plan what should be done in response to them in a proactive manner. This research aims at developing proactive and real-time recovery algorithms for large-scale power networks exposed to weather events considering uncertainty. These algorithms support the recovery decisions to mitigate the disaster impact, resulting in faster recovery of the network. The challenges associated with developing these algorithms are summarized below: Even ignoring uncertainty, when operating cost of the network is considered the problem will be a bi-level optimization which is NP-hard. To meet the requirement for real-time decision making under uncertainty, the problem could be formulated a Stochastic Dynamic Program with the aim to minimize the total cost. However, considering the operating cost of the network violates the underlying assumptions of this approach. Stochastic Dynamic Programming approach is also not applicable to realistic problem sizes, due to the curse of dimensionality.Uncertainty-based approaches for failure modeling, rely on point-generation of failures and ignore the network structure.To deal with the first challenge, in chapter 2, a heuristic solution framework is proposed, and its performance is evaluated by conducting numerical experiments. To address the second challenge, in chapter 3, after formulating the problem as a Stochastic Dynamic Program, an approximated dynamic programming heuristic is proposed to solve the problem. Numerical experiments on synthetic and realistic test-beds, show the satisfactory performance of the proposed approach. To address the third challenge, in chapter 4, an efficient base heuristic policy and an aggregation scheme in the action space is proposed. Numerical experiments on a realistic test-bed verify the ability of the proposed method to recover the network more efficiently. Finally, to address the fourth challenge, in chapter 5, a simulation-based model is proposed that using historical data and accounting for the interaction between network components, allows for analyzing the impact of adverse events on regional service level. A realistic case study is then conducted to showcase the applicability of the approach.

ISBN: 9798535543574Subjects--Topical Terms:

3267268
Annealing.
Subjects--Index Terms:

Disaster Response

Disaster Analytics for Critical Infrastructures : Methods and Algorithms for Modeling Disasters and Proactive Recovery Preparedness.
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