Community resilience-driven restoration model for interdependent infrastructure networks

Karakoc, Deniz Berfin; Almoghathawi, Yasser; Barker, Kash; González, Andrés D.; Mohebbi, Shima

doi:10.1016/j.ijdrr.2019.101228

Cited by 73 publications

(24 citation statements)

References 50 publications

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“…For example, Zhang et al employ an inoperability 2 of 16 input-output model for allocating restoration resources of a disrupted infrastructure network [8]. The other stream denotes the restoration problem as a resource constrained project scheduling problem, which identifies the precedence relationship among all the restoration activities [9,10]. For instance, Nurre et al use a discrete event simulation model to determine how to schedule repair tasks for infrastructure systems [11].…”

Section: Introductionmentioning

confidence: 99%

Resilience-Based Restoration Model for Supply Chain Networks

Mao

Lou²,

Cao

et al. 2020

Mathematics

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An optimal restoration strategy for supply chain networks can efficiently schedule the repair activities under resource limits. However, a wide range of previous studies solve this problem from the perspective of cost-effectiveness instead of a resilient manner. This research formulates the problem as a network maximum-resilience decision. We develop two metrics to measure the resilience of the supply chain networks, i.e., the resilience of cumulative performance loss and the resilience of restoration rapidity. Then, we propose a bi-objective nonlinear programming model, which aims to maximize the network resilience under the budget and manpower constraints. A modified simulated annealing algorithm is employed to solve the model. Finally, a testing supply chain network is utilized to illustrate the effectiveness of the proposed method framework. The results show that the optimal restoration schedule generated by the proposed model is a tradeoff between the cumulative performance loss and the restoration rapidity. Additionally, the sensitivity analysis of parameters indicates that decision-maker’s preference, tolerance factor of delivery time, number of work crews, and availability of budget all have significant impacts on the restoration schedule.

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Section: Introductionmentioning

confidence: 99%

Resilience-Based Restoration Model for Supply Chain Networks

Mao

Lou²,

Cao

et al. 2020

Mathematics

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“…However, the literature, presented in Section 1, has been negligent on this issue. Even when more infrastructure systems are considered as in (Karakoc et al, 2019), the road-network was ignored. Figure 5.…”

Section: Testsmentioning

confidence: 99%

“…By assuming that some disruption affects only the infrastructure network, not the road-network, a heuristic identifies the route with the minimum total traveling time associated with each restoration crew. Still, in mixed-integer programming, Karakoc et al (2019) developed a multi-objective formulation to schedule the restoration of disrupted components from interdependent infrastructure networks relied on socioeconomic and demographic information. Although, the same study suggested, for future works, treating other interdependent infrastructure networks as transportation.…”

Section: Introductionmentioning

confidence: 99%

Electric Power Distribution Maintenance Crews Routing for Fault Location: Time to Bite the Road-Network Disruptions Bullet

Desuó

Fogliatto

Bessani

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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The power distribution system is the most critical, among the power systems, in delivering electricity. Consequently, faults that occur in most cases due to the weather, can cause diverse socio-economic impacts. Hence, considering fault location, the lion's share of the literature addresses maintenance crews patrol routing by merely regarding the power distribution system faults, despite possibly blocked roads or devices accessibility be affected by the weather as well. To properly optimize power distribution system crews inspection routing, the blocked roads must be avoided and the optimization must be conducted to reachable devices. This process is initiated by ltering the blocked roads from the road-network, then a genetic algorithm based on permutation operators is employed on the suitably coded solutions. Furthermore, it was proposed a test case, for the optimization procedure, with a road-network, where the blockedroads were gradually included, and a power distribution system. The resulting solutions showed optimized inspection routes that deviate from blocked roads and skipped from unreachable devices, which is a possible consequence of road-network disruptions. In this manner, they may impact on power distribution maintenance crews routing. Therefore, the suggested methodology proved suitable for a maintenance crew routing under road-network blockage.

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“…Near zero disruption in the operation of infrastructure systems is vital for the delivery and integrity of the essential goods and services [1]. Any interruption in the performance of critical infrastructure systems, such as water distribution, electric power, natural gas, communication, and transportation systems can cause economic loss and affect societal wellbeing [2]. As robustness is referred to as the ability of a system to withstand disturbances [3], robust infrastructure systems are essential for continued societal function and averted economic loss.…”

Section: Introductionmentioning

confidence: 99%

Network Properties for Robust Multilayer Infrastructure Systems: A Percolation Theory Review

2021

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Infrastructure systems, such as power, transportation, telecommunication, and water systems, are composed of multiple components which are interconnected and interdependent to produce and distribute essential goods and services. So, the robustness of infrastructure systems to resist disturbances is critical for the durable performance of modern societies. Multilayer networks have been used to model the multiplicity and interrelation of infrastructure systems and percolation theory is the most common approach to quantify the robustness of such networks. This survey systematically reviews literature published between 2010 and 2021, on applying percolation theory to assess the robustness of infrastructure systems modeled as multilayer networks. We discussed all network properties applied to build infrastructure models. Among all properties, interdependency strength and communities were the most common network property whilst very few studies considered realistic attributes of infrastructure systems such as directed links and feedback conditions. The review highlights that the properties produced approximately similar model outcomes, in terms of detecting improvement or deterioration in the robustness of multilayer infrastructure networks, with few exceptions. Most of the studies focused on highly simplified synthetic models rather than models built by real datasets. Thus, this review suggests analyzing multiple properties in a single model to assess whether they boost or weaken the impact of each other. In addition, the effect size of different properties on the robustness of infrastructure systems should be quantified. It can support the design and planning of robust infrastructure systems by arranging and prioritizing the most effective properties.

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Community resilience-driven restoration model for interdependent infrastructure networks

Cited by 73 publications

References 50 publications

Resilience-Based Restoration Model for Supply Chain Networks

Resilience-Based Restoration Model for Supply Chain Networks

Electric Power Distribution Maintenance Crews Routing for Fault Location: Time to Bite the Road-Network Disruptions Bullet

Network Properties for Robust Multilayer Infrastructure Systems: A Percolation Theory Review

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