Decentralized Decision Making for the Restoration of Interdependent Networks

Talebiyan, Hesam; Dueñas‐Osorio, Leonardo

doi:10.1061/ajrua6.0001035

Cited by 27 publications

(18 citation statements)

References 43 publications

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“…This study is an initial step to couple insights from earthquake engineering with complex cyber-physical systems (CPS). Future directions involve advanced power system adequacy analyses for resilience assessment, the role of decentralized restoration processes (Talebiyan and Duenas-Osorio, 2019, 2020), and the quantification of benefits from temporary solutions seen in the field, such as Cell on Wheels (COWs).…”

Section: Discussionmentioning

confidence: 99%

Congestion and observability across interdependent power and telecommunication networks under seismic hazard

Talebiyan

Leelardcharoen²,

Dueñas‐Osorio

et al. 2021

Earthquake Spectra

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This article quantifies the seismic performance of interdependent electric power and telecommunication systems, while also identifying variables with the highest impact on design. We introduce interdependent power and telecommunication models, which probabilistically simulate the physical dependency of telecommunication systems on power via interdependent adjacency and coupling strength, while a topology observability analysis quantifies the cyber dependency of the power system on telecommunications. We also use new functionality-based performance measures, including data congestion in telecommunications and partial observability in power systems, given communication demands upsurging after earthquakes. As an application, our methodology assesses the performance of stylized power and telecommunication systems in Shelby County, TN. Results show that neglecting retrials, congestion, and power interdependency lead to significant overestimation of the performance of telecommunication systems, particularly at low-to-medium hazard levels. Sensitivity results also reveal that decreasing the strength of coupling across systems is one of the most effective ways to improve the seismic performance of evolving cyber-physical systems, particularly when increasing observability in the power system through telecommunication end offices with richer data flow pathways.

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Section: Discussionmentioning

confidence: 99%

Congestion and observability across interdependent power and telecommunication networks under seismic hazard

Talebiyan

Leelardcharoen²,

Dueñas‐Osorio

et al. 2021

Earthquake Spectra

Self Cite

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“…Although this setting is more realistic, the associated complexity increases the amount of required information for the decision maker to plan. Some studies in the literature have addressed the decentralized network optimization problem for infrastructures (e.g., see He et al, 2017;Talebiyan and Duenas-Osorio, 2020). Nonetheless, the resulting models are mainly applicable to small or county-level networks, due to the assumption that all actors are individually making decisions (Talebiyan and Duenas-Osorio, 2020).…”

Section: Optimization In Interdependent Infrastructuresmentioning

confidence: 99%

“…Some studies in the literature have addressed the decentralized network optimization problem for infrastructures (e.g., see He et al, 2017;Talebiyan and Duenas-Osorio, 2020). Nonetheless, the resulting models are mainly applicable to small or county-level networks, due to the assumption that all actors are individually making decisions (Talebiyan and Duenas-Osorio, 2020). Smith et al (2020) investigated noncooperative decision making when information is incomplete and two decision makers cannot negotiate.…”

Section: Optimization In Interdependent Infrastructuresmentioning

confidence: 99%

Decentralized resource allocation for interdependent infrastructures resilience: a cooperative game approach

Mohebbi

Barnett

Aslani

2021

Int Trans Operational Res

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Interdependent critical infrastructures are governed by several sectors working together to maintain social, economic, and environmental well-being. Although many models focus on a centralized view for networks for the restoration planning of these networks, rarely is there only one decision maker for the infrastructure networks. In the decentralized decision-making paradigm, individual decision makers need to decide how to prioritize areas of the network and eventually improve the aggregated infrastructure systems resilience. There is a dearth of quantitative studies analyzing resource allocation decisions considering both decentralized and cooperative aspects. This paper aims to propose a coalitional game theory approach to address decentralized resource allocation for interdependent water distribution and road networks. In particular, combining coalitional game theory with weighted graphs creates an order of repair for each node in the coalitions. Subsequently, the decision makers can pass the information on to the master problem, reducing the complexity of the resource allocation problem for the interdependent networks. The proposed approach is applied to water distribution and transportation networks in the City of Tampa, Florida. We compare the decentralized solutions to centralized solutions in different scenarios to demonstrate the feasibility of our approach for the city-scale networks. The results indicated the superiority of the proposed framework in terms of computational time and solution quality.

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“…where arguments of cost functions are introduced in Table 1, and, for our application, their ranges are adopted from the literature [12,35]. The cost functions are linear, and sum up to their respective cost over all elements across the network for a given time-step t. The feasible space of the solution is defined by several constraints, which can be categorized into five groups:…”

Section: Td-indp Algorithmmentioning

confidence: 99%

Deep Learning-based Resource Allocation for Infrastructure Resilience

Alemzadeh¹,

Talebiyan²,

Talebi³

et al. 2020

Preprint

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From an optimization point of view, resource allocation is one of the cornerstones of research for addressing limiting factors commonly arising in applications such as power outages and traffic jams. In this paper, we take a data-driven approach to estimate an optimal nodal restoration sequence for immediate recovery of the infrastructure networks after natural disasters such as earthquakes. We generate data from td-INDP, a high-fidelity simulator of optimal restoration strategies for interdependent networks, and employ deep neural networks to approximate those strategies. Despite the fact that the underlying problem is NP-complete, the restoration sequences obtained by our method are observed to be nearly optimal. In addition, by training multiple models-the so-called estimators-for a variety of resource availability levels, our proposed method balances a trade-off between resource utilization and restoration time. Decision-makers can use our trained models to allocate resources more efficiently after contingencies, and in turn, improve the community resilience Besides their predictive power, such trained estimators unravel the effect of interdependencies among different nodal functionalities in the restoration strategies. We showcase our methodology by the real-world interdependent infrastructure of Shelby County, TN.

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Decentralized Decision Making for the Restoration of Interdependent Networks

Cited by 27 publications

References 43 publications

Congestion and observability across interdependent power and telecommunication networks under seismic hazard

Congestion and observability across interdependent power and telecommunication networks under seismic hazard

Decentralized resource allocation for interdependent infrastructures resilience: a cooperative game approach

Deep Learning-based Resource Allocation for Infrastructure Resilience

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