An Exact Multiobjective Optimization Approach for Evaluating Water Distribution Infrastructure Criticality and Geospatial Interdependence

Abdel-Mottaleb, Noha; Saghand, Payman Ghasemi; Charkhgard, Hadi; Zhang, Q.

doi:10.1029/2018wr024063

Cited by 21 publications

(9 citation statements)

References 48 publications

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“…break pipes or add leaks). WNTR has also been used to simulate other disaster scenarios such as pipe breaks (Logan et al, 2021;Mazumder et al, 2020;Tomar et al, 2020), power loss or source isolation (Abdel-Mottaleb et al, 2019), and cyber-security related incidences (Moraitis et al, 2020;Nikolopoulos et al, 2021). The results from these types of applications can be used to identify important system components that help improve system resilience.…”

Section: Water Network Tool For Resiliencementioning

confidence: 99%

Embracing Analytics in the Drinking Water Industry

2022

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Analytics can support numerous aspects of water industry planning, management, and operations. Given this wide range of touchpoints and applications, it is becoming increasingly imperative that the championship and capability of broad-based analytics needs to be developed and practically integrated to address the current and transitional challenges facing the drinking water industry. Analytics will contribute substantially to future efforts to provide innovative solutions that make the water industry more sustainable and resilient. The purpose of this book is to introduce analytics to practicing water engineers so they can deploy the covered subjects, approaches, and detailed techniques in their daily operations, management, and decision-making processes. Also, undergraduate students as well as early graduate students who are in the water concentrations will be exposed to established analytical techniques, along with many methods that are currently considered to be new or emerging/maturing. This book covers a broad spectrum of water industry analytics topics in an easy-to-follow manner. The overall background and contexts are motivated by (and directly drawn from) actual water utility projects that the authors have worked on numerous recent years. The authors strongly believe that the water industry should embrace and integrate data-driven fundamentals and methods into their daily operations and decision-making process(es) to replace established “rule-of-thumb” and weak heuristic approaches – and an analytics viewpoint, approach, and culture is key to this industry transformation. ISBN: 9781789062373 (paperback) ISBN: 9781789062380 (eBook) ISBN: 9781789062397 (ePub)

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Section: Water Network Tool For Resiliencementioning

confidence: 99%

Embracing Analytics in the Drinking Water Industry

2022

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“…To the best of our knowledge, only a limited number of studies considered the city‐scale interconnected networks of water and transportation systems (see Abdel‐Mottaleb et al. (2019)). In these papers, restoration planning is not addressed, though.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, external stressors such as natural disasters (floods, hurricanes, and extreme weather) and human‐related activities (terrorism and vandalism) always jeopardize the standard functionality of these networks (Mohebbi et al., 2020). In specific, water distribution networks are under continuous pressure due to population growth, extreme weather patterns, and aging infrastructure (Abdel‐Mottaleb et al., 2019). Transportation systems are also particularly vulnerable to extreme natural events during which many roads and intersections might remain closed (Huang et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Learn to decompose multiobjective optimization models for large‐scale networks

Aslani

Mohebbi

2022

Int Trans Operational Res

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Infrastructures can be modeled as large‐scale networks consisting of nodes and arcs, making network optimization a popular modeling option for arising problems. In specific, providing timely restoration plans for interdependent infrastructures facing disruptions has been a challenge for decision makers. In this study, we focus on geospatial (co‐location) and functional interdependencies to capture the impact of cascading failures on infrastructure systems. The dynamics of real networks are more complicated to be captured by one objective function. Therefore, we define three objective functions in three pillars of sustainability: (a) economic, (b) social, and (c) environmental. To solve the multiobjective optimization model, we develop a learn‐to‐decompose framework, consisting of a multiobjective evolutionary algorithm based on decomposition module and a Gaussian process regression (GPR) module to periodically learn from the obtained Pareto front and guide the search direction. We also included a heuristic module to address two significant challenges in restoring interdependent infrastructures: the island scenario and co‐location interdependencies. We applied the proposed framework to benchmark problems and interdependent water and transportation networks in the City of Tampa, FL. We carried out sensitivity analyses to monitor the performance of the GPR by different kernel functions. We also provided insights for decision makers by finding the trade‐off between fortification (proactive) and restoration (reactive) costs. The result demonstrates the proposed framework is feasible and applicable for large‐scale networks.

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“…It should be noted that for each city-scale infrastructure network, key nodes are distributed and independent, that is, they do not directly interact. This is due to the fact that the field validated network models are created by following skeletonization procedures, that is, merging original nodes/links by applying the combination of attributes to the newly merged nodes/links (see Santana, 2015;Abdel-Mottaleb et al, 2019). Hence, to simplify the presentation of the game and weighted graphs, we consider one key node per coalition.…”

Section: Unanimity Gamementioning

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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An Exact Multiobjective Optimization Approach for Evaluating Water Distribution Infrastructure Criticality and Geospatial Interdependence

Cited by 21 publications

References 48 publications

Embracing Analytics in the Drinking Water Industry

Embracing Analytics in the Drinking Water Industry

Learn to decompose multiobjective optimization models for large‐scale networks

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

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