Interdependent Infrastructure Research Articles

A Model for Optimal Economic Lockdown to Contain Epidemic

This paper is concerned with the application of operations research in defining the optimal lockdown of economic activities to contain epidemic. The problem of optimal lockdown consists in deciding as best economic sectors can be lockdown with respect to fundamental sectors (essential goods and services) while disruptive impacts are minimized on the economy as a whole. Many countries around the world are currently implementing the lockdown of most economic activities to contain the spread of the Covid-19 pandemic. The lockdown brings health benefits for the society as it contains the spread of the virus, reducing the number of infections and allowing the health system to treat those infected better. This paper describes a Boolean linear programming model to deal with the problem of selecting several economic sectors to be shutdown. The objective function is linear and the constraints are linear inequalities related to the Leontief’s input-output table. The model permits to analyze the feasibility of national economic system in which some elements of the input-output table are set equal to zero. The mathematical approach to the shutdown problem permits to identify the greatest number of economic sectors that can be closed without destroying the fundamental sectors. Since solution of the shutdown problem is the greatest number of lockdown economic sectors, author believes the model allows to oppose effectively the spread of virus. Once spread of the virus decreases, another feature of the model is to support decision makers in assigning priorities to the economic sectors to be gradually unlocked. Standard input-output models are able to reveal how different sectors of an economy are interconnected and how changes in one sector affect all other sectors. Besides the use of the input-output data for descriptive analyses of economic interrelations, this table also provides the empirical fundament for a wide scale of impact analysis. input- output models greatly differ in size and possible applications. Simple static input-output models are used for comparative-static impact (scenario) analysis. With the help of input-output quantity models, statements can be made about direct and indirect effects based on exogenous changes in demand. More complex dynamic input-output models largely resolve the limitations and inherent assumptions of static input-output models. Time is considered explicitly; quantity and price reactions are modelled endogenously in a holistic approach and feedback effects are captured. Last models are not only suitable for scenario analysis and for forecasting as well. In “A Linear Programming Solution to Dynamic Leontief type Models” Harvey M. Wagner presents a general dynamic model of an economy and investigates a number of questions related to the feasibility of certain time profiles of demand and the rate of substitution between economic activities. The Inoperability Input-Output Model [1-4]. was developed to understand better the infrastructure interdependencies. Based on the Leontief Input- Output Model, the model is demand-driven, wherein perturbations to the final demand levels are considered the initiating event, and the impact to sectors’ production outputs are the direct and indirect effects resulting from sector interdependencies. Case study shows that the different perturbation classes (demand or supply, quantity or price) yield different rank orders of sectors impacted by the initiating event, thus providing various perspectives of impacts. The present study differs from most previous studies in two aspects. First, linear programming techniques have been widely used in Input-Output model: from the primary and dual formulation of the problem to the analysis of Lagrange multipliers in order to assess the mutual relative importance of sectors. Second, the impact of lockdown is measured in terms of annihilated interdependencies rather than in terms of canceled economic values such as revenues and monetary exchanges. For this reason, the proposed model takes into account the adjacency matrix got from input-output Table rather than the Table itself. This analysis is static in character (it is a static I-O model) because it does not take care explicitly of time, quantity and price reactions, consumption and production lags, growth of final demand (these ones are known as dynamic I-O models). The study simply considers the interruption of several activity as a consequence of a government measure adopted to reduce the risk of contagion. The proposed model answers to the basic question if the annihilation of N sectors is feasible, secondary the model identifies in the Lagrange multipliers the means to sequencing the sectors to be reopened.

A methodological approach for mapping and analysing cascading effects of flooding events

Current local or regional flood risk assessments, as required by the EU flood risk directive, rarely account for cascading effects due to interdependencies between critical infrastructures. However, it is essential to consider these effects, as they may severely impact areas outside the immediate flood risk area. The main purpose is to present and problematise a method (AB-CEM) for mapping and analysing cascading effects due to floods, aiming at also being relevant for other spatially widespread hazards. The method development and the pilot study, in Sweden, reveal that there is a prominent practical need for methods for mapping and analysing critical infrastructure interdependencies and cascading effects. Another key finding is that the method process and its results can serve as an important basis for decision-making about proactive and reactive efforts related to geographically extensive hazards. We further conclude that there is a recurring problem regarding sensitive and secret data. More specifically, the conflicting interests of information availability and information security concerning critical infrastructures, which needs to be resolved at the national level and communicated through clear guidelines. The method is a much-needed step towards accounting for cascading effects of floods in practice.

Nested Decomposition Approach for Dispatch Optimization of Large-Scale, Integrated Electricity, Methane and Hydrogen Infrastructures

Energy system integration enables raising operational synergies by coupling the energy infrastructures for electricity, methane, and hydrogen. However, this coupling reinforces the infrastructure interdependencies, increasing the need for integrated modeling of these infrastructures. To analyze the cost-efficient, sustainable, and secure dispatch of applied, large-scale energy infrastructures, an extensive and non-linear optimization problem needs to be solved. This paper introduces a nested decomposition approach with three stages. The method enables an integrated and full-year consideration of large-scale multi-energy systems in hourly resolution, taking into account physical laws of power flows in electricity and gas transmission systems as boundary conditions. For this purpose, a zooming technique successively reduces the temporal scope while first increasing the spatial and last the technical resolution. A use case proves the applicability of the presented approach to large-scale energy systems. To this end, the model is applied to an integrated European energy system model with a detailed focus on Germany in a challenging transport situation. The use case demonstrates the temporal, regional, and cross-sectoral interdependencies in the dispatch of integrated energy infrastructures and thus the benefits of the introduced approach.

Model and solution method for mean-risk cost-based post-disruption restoration of interdependent critical infrastructure networks

Critical infrastructure networks (CINs), such as power grids, water distribution systems, and telecommunication networks, are essential for the functioning of society and the economy. As these infrastructure networks are not isolated from each other, their functions are not independent and may be vulnerable to disruptive events (e.g., component failures, terrorist attacks, natural disasters). For decision makers, how to restore the functions of CINs while accounting for interdependencies and various uncertainties becomes a challenging task. In this work, we study the post-disruption restoration problem for a system of interdependent CINs under uncertainty. We propose a two-stage mean-risk stochastic restoration model using mixed-integer linear programming (MILP) with the goal of minimizing the total cost associated with unsatisfied demands, repair tasks, and flow of interdependent infrastructure networks. The restoration model considers the availability of limited time and resources and provides a prioritized list of components to be restored along with assigning and scheduling them to the available network-specific work crews. Additionally, the model features flexible restoration strategies including multicrew assignment for a single component and a multimodal repair setting along with the consideration of full and partial functioning and dependencies between the multi-network components. The proposed model is illustrated using the power and water networks in Shelby County, Tennessee, United States, under two hypothetical earthquake scenarios.

Multi-infrastructure energy systems resiliency assessment in the presence of multi-hazards disasters

The continuous operation of critical infrastructures such as electricity, gas, and water networks is one of the major fundamentals of a sustainable city considering their interdependency. Regarding the interdependency of these infrastructures, an interruption in each one can cause severe deficiencies and a lack of energy supply sustainability in the other infrastructures. Additionally, multi-hazard disasters have always been a threat to a well-constructed multi-infrastructure energy system (MIESs). MIESs can simultaneously be exposed to multiple high impacts of different disasters with a low probability. Its consequences may overlap with other hazards and lead to temporal or permanent damages for infrastructures’ components. This paper studies the resiliency of a MIES, including the interdependence of natural gas and electricity networks affected by multi-hazard disasters such as hurricane and earthquakes. The impacts of different hazards with various levels are modeled using multi-phase performance response curves to determine the time-dependent performance levels of the MIES in different phases. The impacts of hazard levels on component and system performance are analyzed, and node resilience criteria are employed for various energy subsystems to evaluate the resilience of the MIES. Based on the results, the simultaneous occurrence of the different hazards on interdependent infrastructures can boost and overlap the impact of a single hazard on the MIES.

High level architecture-based framework for modeling interdependent critical infrastructure systems

Interdependencies among urban critical infrastructure systems (CISs) significantly impact the reliability and performance of CISs and the resilience of modern societies. Although several approaches exist for modeling interdependent CISs and studying their behavior, models developed in previous studies often fail to incorporate CIS domain knowledge, capture systemic heterogeneities among the CISs, and accurately model CISs interdependencies. Consequently, existing models have a limited ability to simulate interdependent CISs with sufficient detail and accuracy. To address these limitations, this study proposes a high-level architecture (HLA)-based framework for modeling interdependent CISs that can leverage and integrate well-tested practices, knowledge, data and simulation tools accumulated over years of wide usage in various CIS domains. The framework provides a methodology for co-simulating heterogeneous fine-grained CIS domain-specific models and modeling complex interactions between them and with their external environments, hence reproducing with high fidelity the complex coupled systems. A case study of two interdependent power and water systems was conducted, which demonstrated the efficacy of the proposed framework. Simulation results revealed that the HLA-based CISs model could capture the heterogeneous behaviors of the CISs and reveal a variety of failure-induced system vulnerabilities and feedback loops which may not be observable when using other existing modeling approaches.

Decision support architectures for the recovery of interdependent infrastructure with large‐scale damage and socially vulnerable populations

AbstractInfrastructure damage has household‐level consequences after a major disaster. Losses are experienced due to factors such as unavailable services and impaired mobility. Socially vulnerable residents, in particular, have few resources with which to adapt. Decision support tools for making justifiable, transparent, repeatable decisions that center the needs of users during recovery are currently nonexistent. In part, this is because infrastructure recovery is a complex process, often involving the coordination of multiple entities. The recovery problem can be rendered more tractable by applying tools suitable for modeling complex systems and processes. System theoretic process analysis (STPA) can be used for goalsetting in a complex, dynamic system such as community civil infrastructure. STPA is used here to devise a decision support tool architecture suitable for coordinated multiagency recovery efforts. The example application is a long‐term recovery process with widespread infrastructure damage, population displacement, and other disruptions in system use due to a major disaster. In the example, losses and hazards are defined to reflect recovery challenges commonly faced by vulnerable populations experiencing partial or total displacement. This extension of STPA then reverses these hazards, starting with the most hazardous system states and progressing sequentially to less hazardous states until recovery is complete.

Quantifying disaster resilience of a community with interdependent civil infrastructure systems

Disaster resilient civil infrastructure systems are essential for disaster resilient communities. Measuring the resilience of these systems is the first step towards their improvement. This, however, is not easy: civil infrastructure systems are highly complex, operate in different ways, and are affected differently in different disasters. Adding to the complexity are the dynamic interdependencies among components of such systems, that change as the community is recovering. The Re-CoDeS framework for quantifying disaster resilience measures the lack of resilience of a system (e.g., a community) as the amount of the system’s unmet demand for a considered resource or service over the resilience assessment interval. This paper presents a novel demand/supply-based method to consider dynamic component interdependencies by extending the Re-CoDeS framework: whenever the demand of a component is not met by the currently available supply capacity of the system, that component ceases to operate and its supply capacity decreases. The proposed iRe-CoDeS framework is demonstrated on a virtual community served by three interdependent civil infrastructure systems producing five types of resources and services. Step-by-step instructions for defining a community model within the iRe-CoDeS framework are presented to facilitate the use of the proposed framework.

Resilience of Critical Infrastructure Systems to Floods: A Coupled Probabilistic Network Flow and LISFLOOD-FP Model

In this paper, a network-flow model was constructed to simulate the performance of interdependent critical infrastructure systems during flood hazards, when there is shortage of commodities such as electrical power and water. The model enabled us to control the distribution of commodities among different consumers whose demand cannot be fully met. Incorporating time-variance in the model allowed for evaluating the time evolution of the functional level of the infrastructure systems and quantifying their resilience. As a demonstration of the model’s capability, the network model was coupled with a raster-based hydraulic flooding model in the way of Monte Carlo simulations. It was then used to investigate the cascading effects of flood-related failures of individual infrastructure assets on the performance of the critical infrastructure systems of a coastal community under different flooding scenarios and future climate impacts. The coupled modelling framework is essential for correctly assessing the interdependences and cascading effects in the infrastructure systems in the case of flood hazards. While in the considered example, the extent of inundation becomes less severe with a changing climate, the risk to infrastructure does not recede because of the cascading effects. This behaviour could not be captured by the flood model alone.

Logistics Cluster as a Way of Implementing a Multisourcing Model to Ensure the Livelihood of the Local Community

In recent years, the living conditions of territorial communities in Ukraine promote the integration of organizations and enterprises in various fields. The cluster is a group of localized interdependent companies, equipment suppliers, components, specialized services, infrastructure, research institutes, universities and other organizations that complement and enhance each other's competitive advantages. Current common problems that hinder the normal functioning of local communities are: utilities and energy efficiency, investment climate and investment, public safety, problems of access to administrative services, communication problems between the leadership of the local community and local population, environmental issues. Based on the results of the study of the internal environment of the united territorial community, the benefits of cluster approach and the benefits that can provide a multisourcing model, it is determined that the main problems that hinder the normal functioning of modern communities can be solved by forming a logistics cluster. The logistics cluster will allow to fully implement the multisourcing model of ensuring the livelihood of the local community. Such model of decision-making on the rational choice of source of supply from several alternatives will: improve the quality of supply management of goods / services; reduce the logistics cycle; reduce supply costs; increase the reliability of supplies, etc. Thus, the formation of a logistics cluster at the level of ensuring the livelihood of the territorial community helps to strengthen the competitiveness of each of the participants. The main participants of the cluster are: production and logistics companies located in the most important centers of intersection and origin of goods and goods, directly involved in the process of providing services.

Predicting Resilience of Interdependent Urban Infrastructure Systems

Climate change is increasing the frequency and the intensity of weather events, leading to large-scale disruptions to critical infrastructure systems. The high level of interdependence among these systems further aggravates the extent of disruptions. To mitigate these impacts, models and methods are needed to support rapid decision-making for optimal resource allocation in the aftermath of a disruption and to substantiate investment decisions for the structural reconfiguration of these systems. In this paper, we leverage infrastructure simulation models and Machine Learning (ML) algorithms to develop resilience prediction models. First, we employ an interdependent infrastructure simulation model to generate infrastructure disruption and recovery scenarios and compute the resilience value for each scenario. The infrastructure-, disruption-, and recovery-related attributes are recorded for each scenario and ML algorithms are employed on the synthetic dataset to develop accurate resilience prediction models. The results of the prediction models are analyzed and possible design strategies suggested based on the resilience enhancement attributes. The proposed methodology can support infrastructure agencies in the resource-allocation process for pre- and post-disaster interventions.

Real-Time Resilience Optimization Combining an AI Agent With Online Hard Optimization

In the highly interdependent environment of a large city, failures in the Electrical Distribution System (EDS) can cause direct or indirect consequences to other critical infrastructures and the well-being of the citizens. To increase the resilience of the supply of electricity to the city, this work combines the pre-training of an AI agent and very fast calculation of the optimum recovery path after the number and location of the electrical faults are known. In the introduced Soft-Hard Optimal Convergence (SHOC) method, machine learning techniques are used to train an AI agent with thousands of off-line scenarios for optimum system restoration. In real-time, after the actual fault information is known, the agent will provide a subset of solutions (soft solution) to be considered for hard optimization algorithms. The Infrastructure Interdependencies Simulator (i2SIM) is used to assist the prioritization of the sequence of fault recovery and topological reconfiguration to minimize the black-out time of the most critical loads. A 70-node distribution system case is used to demonstrate the proposed methodology, with solution times in the order of seconds to find the optimum repair sequence and switches topological reconfiguration to optimize the city's resilience index.

Modelling of multi-sectoral critical infrastructure interdependencies for vulnerability analysis

Critical infrastructure such as the transportation, power generation, water supply, telecommunications, security and health services/systems, etc. are essential for providing a reliable flow of goods and services, crucial to the functioning of the economy and society. These infrastructures are closely linked and dependent on one another, and these interdependencies need to be modelled in order to analyse the disruptions and vulnerabilities of critical infrastructure networks as a whole. With increased, investment and complexity in the coupling of gas and electricity network, limitations and vulnerabilities of the coupled networks are becoming increasingly relevant to the operational planning of the critical infrastructures. Current modelling of a coupled gas and electricity network will be used in conjunction with nation input-output interdependency model to model physical critical infrastructures and critical infrastructure interdependencies, respectively. This research work will tackle two possible scenarios that might happen in the gas network while evaluating the cascading impact both in the physical model perspective and input-output interdependency model perspective. The results will provide insights on how disruption in the gas network affects the electricity grid and its corresponding economic impact on all economic sectors in a nation.

Socio-Technical Infrastructure Interdependencies and Their Implications for Urban Sustainability; Recent Insights from the Netherlands

Socio-Technical Infrastructure Interdependencies and Their Implications for Urban Sustainability; Recent Insights from the Netherlands

Simulating the role of transportation infrastructure for community disaster recovery

Functional recovery of transportation infrastructure after a disaster is essential for community disaster resilience, as the recovery of damaged community components depends on their accessibility for repair. This paper presents a community disaster recovery simulation that accounts for community components' accessibility for repair using a demand–supply framework. The considered components of a community are viewed as suppliers and/or users of various resources and services essential for community functionality, reflected in components’ supply and demand properties. Whenever the demand of a component is not met, that component ceases to operate, simulating interdependency effects. Similarly, recovery demand is attributed to damaged components, representing the amounts of resources and services (e.g. workers, machinery and transportation services) that these components need to recover. The proposed framework is illustrated on a virtual community with 3600 inhabitants supported by several interdependent infrastructure systems. The results show that the transportation network damage slows down the recovery of the virtual community by preventing access to damaged components and reducing the ability of the community to mobilise available repair resources. Furthermore, the effect of such prolonged transportation system recovery on the damage-free infrastructure systems whose functionality was decreased due to their dependency on the affected infrastructure systems, is quantified.

Risk-Informed Decision-Making for Predisaster Risk Mitigation Planning of Interdependent Infrastructure Systems: Case Study of Jamaica

Hazards cause huge social disruptions and economic losses each year. Community disaster resilience relies on the capacity of critical infrastructure systems to maintain acceptable levels of functionality during a disruptive event and to recover promptly after the event. A proper predisaster risk mitigation planning framework can aid communities by working in advance to understand the preexisting local needs, identify the leaders, form partnerships, establish resources, and seize opportunities to increase community disaster resilience. Although numerous theoretical studies exist on developing models to support infrastructure predisaster risk mitigation, these models have not been properly incorporated into the decision-making process in existing studies. This lack of incorporation impedes the transfer of theoretical knowledge to practical policymaking. High-level predisaster infrastructure risk mitigation decisions for multiple infrastructure systems are especially in need of such an illustration due to the complexity introduced by the scale. This paper defines the interdependent infrastructure risk mitigation (IIRM) problem in a risk-informed decision-making framework and illustrates the entire decision-making process using a case study on predisaster risk mitigation planning of the interdependent critical infrastructure systems in Jamaica. This region is vulnerable to natural hazards but has received little attention in the literature.

Local interactions and homophily effects in actor collaboration networks for urban resilience governance

Understanding actor collaboration networks and their evolution is essential to promoting collective action in resilience planning and management of interdependent infrastructure systems. Local interactions and choice homophily are two important network evolution mechanisms. Network motifs encode the information of network formation, configuration, and the local structure. Homophily effects, on the other hand, capture whether the network configurations have significant correlations with node properties. The objective of this paper is to explore the extent to which local interactions and homophily effects influence actor collaboration in resilience planning and management of interdependent infrastructure systems. We mapped bipartite actor collaboration network based on a post-Hurricane Harvey stakeholder survey that revealed actor collaborations for hazard mitigation. We examined seven bipartite network motifs for the mapped collaboration network and compared the mapped network to simulated random models with same degree distributions. Then we examined whether the network configurations had significant statistics for node properties using exponential random graph models. The results provide insights about the two mechanisms—local interactions and homophily effect—influencing the formation of actor collaboration in resilience planning and management of interdependent urban systems. The findings have implications for improving network cohesion and actor collaborations from diverse urban sectors.

The role of concurrent engineering in resilient critical infrastructures during disasters

The world has complex mega-cities and interdependent infrastructures. This complication in infrastructure relations makes it sensitive to disasters and failures. Cascading failure causes blackouts for the whole system of infrastructures during disasters and the lack of performance of the emergency management stakeholders is clear during a disaster due to the complexity of the system. This research aimed to develop a new concurrent engineering model following the total recovery effort. The objectives of this research were to identify the clustered intervention utilized in the field of resilience and developing a cross-functional intervention network to enhance the resilience of societies during a disaster. Content analysis was employed to classify and categorize the intervention in the main divisions and sub-divisions and the grouping of stakeholders. The transposing system was employed to develop an integrated model. The result of this research showed that the operations division achieved the highest weight of information interchange during the response to improve the resilience of the system. The committee of logistics and the committee of rescue and relief needed the widest bandwidth of information flow in the concurrent engineering (CE) model. The contributed CE model helped the stakeholders provide a resilient response system. The final model and the relative share value of exchanging information for each workgroup can speed up recovery actions. This research found that concurrent engineering (CE) is a viable concept to be implemented as a strategy for emergency management. The result of this research can help policymakers achieve a collaborative teamwork environment and to improve resilience factors during emergency circumstances for critical infrastructures.

Investigating the importance of critical infrastructures' interdependencies during recovery; lessons from Hurricane Irma in Saint-Martin's island

Most research on critical infrastructure (CI) interdependencies focus primarily on their role in propagating system disruptions, neglecting their role during the restoration and recovery processes. The few studies incorporating interdependencies to model restoration, often consider hypothetical situations, cover a maximum of five CIs, or focus on short-term recovery. In an effort to bridge this gap, the presented study investigates the influence of CI interdependencies on the restoration of Saint Martin's CI network-of-networks after Hurricane Irma. Daily restoration data of 15 CIs was collected to build service restoration curves over a period of 28 months (medium-to long-term recovery). Obtained recovery curves revealed that while Irma took a proportionate toll on all Saint-Martin's CI, the latter's recovery took a surprisingly long time. Our investigation found underlying interdependencies that were ignored in the plans, which could have led to a slower system restoration process. Accordingly, a holistic “network-of-networks”-based approach coupled with statistical modeling was followed to rank CI according to their criticality degree and correspondingly simulate a criticality-based systemic restoration plan. Key findings reveal that, in order to optimize CI recovery, the conventional silo-based and decentralized CI-by-CI restoration approaches should be complemented by a well-coordinated restoration strategy that recognizes and considers interdependencies.

Joint optimization of structure and protection of interdependent infrastructure networks

Nowadays, critical infrastructures are essential for the normal operation of economics and social life. Because of the increasing number of terrorist attacks and other accidents, research on strengthening infrastructure protection has become essential. Meanwhile, the operation of many types of infrastructures mainly relies on the configuration of networks. The interdependent infrastructure network (IIN), which is a network comprising multiple infrastructure networks with different functions under supporting relationships, is highly influenced by the interdependency structure of these networks and the protection strategy. To better understand the relation between the interdependency structure and protection in two phases on infrastructure resilience and analyze their influence on IIN performance, this study investigates the IIN in a community and proposes a joint optimization method for the two phases based on an incomplete-information game under the situation of intentional attacks. Employing the maximal-flow index (MFI) as the performance metric, a multi-objective planning approach is proposed for the joint optimization of interdependency structure and protection. Furthermore, an improved probabilistic solution discovery algorithm (PSDA) is developed to solve the planning model, and the joint optimization is analyzed using extensive experimentation, including three real-world infrastructure examples.