Inoperability Input-output Model Research Articles

Inoperability Input-Output Modeling: Inventory Optimization and Resilience Estimation during Critical Events

AbstractAssessing the ability of critical infrastructures to overcome shocks and optimizing their preparedness for critical events is a key factor in reducing damage, ensuring resilience, and mitigating monetary losses. When considering the problem in a broad sense, the assessment of technical dependencies among engineered systems can be supported by the analysis of economical relationships. A key tool to accomplish this objective is to exploit the input-output approach proposed by Wassily Leontief for the quantitative analysis of the relationships among different branches of an economy. Recently, the input-output approach was effectively exploited to assess the resilience of economies against critical events that may affect some sectors and ripple through neighboring economic segments as a function of their vulnerability, inertia, and centrality to the overall economy. Building on an existing approach, this paper considers a dynamic inoperability input-output model with inventories, examined according to...

Risk assessment of the economic impacts of climate change on the implementation of mandatory biodiesel blending programs: A fuzzy inoperability input–output modeling (IIM) approach

Many countries have implemented biofuel programs designed to address pressing concerns such as climate change, energy security and rural development. However, recent works suggest that biofuel resources may be at risk due to climate-induced disruptions such as changes in precipitation levels, pest infestation, or increased frequency of extreme weather events. The incidence of such disruptions not only affects biofuel producers, but also energy-dependent economic sectors, resulting in “ripple effects” that further increase economic losses. A variant of the inoperability input–output model (IIM) is used to assess the economic effects of implementing mandatory biodiesel blending programs in the Philippines. This approach is an extension of input–output analysis that quantifies risk through the dimensionless inoperability metric, whose value ranges from 0 to 1 depending on the degree of failure. Using the IIM, we estimate the resulting crop losses using the storm damage and pest infestation scenarios at the proposed blending rate of 5% currently being considered in the Philippines. Uncertainties within the modeling framework are captured using fuzzy numbers. Different ranking strategies are then evaluated to determine sector vulnerability using inoperability levels and economic losses. The effect of uncertainties is also taken into account through fuzzy ranking of the sectors.

Estimating economic loss from cascading infrastructure failure: a perspective on modelling interdependency

Infrastructure failure can cause significant disruption of economic activity. The size of economic loss is a direct function of the interdependencies between infrastructure and economic systems raising important questions about infrastructure vulnerability and resilience. Economic theory is important in this regard as it makes a distinction between damage to infrastructure (stock) and how this may transfer to losses in economic productivity (flow). In order to capture the economic consequences of infrastructure failure, various economic models have been proposed to represent the multimodal complex networks and capture the effects of cascading infrastructure failure. There is still no consensus on the correct approach for estimating economic loss. The method commonly known as input-output analysis has gained the most attention in recent years for its ability to model indirect or higher-order economic losses. The typical input-output approach has spawned an entire field of related models which include: the inoperability input-output model (IIM); Ghosh supply-side model; dynamic input-output models; key-linkages analysis; as well as inventory based models amongst others. Amongst the various methods used to model infrastructure failure this paper identifies the assumptions and shortcomings that must be overcome to produce better estimates of economic loss. Firstly, critical infrastructure systems are connected to the economy through both physical and economic linkages. Models need to capture both types of linkage to accurately represent how cascading infrastructure failure will lead to economic loss and then how sectoral losses may have an indirect impact on infrastructure systems. Secondly, input-output based approaches assume that the economic structure within an economy remains stable during a disaster and throughout the recovery process. New models are required that are able to capture substitution of goods and structural change within an economy. Thirdly, models of economic loss are generally deterministic in nature and thus give no indication about the uncertainty behind model-based estimates. Economic loss estimates using probability theory and methods such as Monte-Carlo simulations or fuzzy logic may prove to be important avenues for quantifying uncertainty in economic loss estimates resulting from infrastructure failure.

REFLECTIONS ON THE INOPERABILITY INPUT–OUTPUT MODEL

We argue that the inoperability input–output model is a straightforward – albeit potentially very relevant – application of the standard input–output model. In addition, we propose two less standard input–output approaches as alternatives to take into consideration when analyzing the effects of disasters or disruptions.

Impact of political dispute on international trade based on an international trade Inoperability Input-Output Model: A case study of the 2012 Diaoyu Islands Dispute

While political disputes occur frequently and widely among many countries, their impact on the international trade is unclear and less systematically investigated. Considering the 2012 Diaoyu Islands Dispute, under several premised assumptions, this paper applies the international trade Inoperability Input-Output Model to determine the indirect economic loss and to screen out Chinese industries that are sensitive to the dispute. Results based on Leontief's technical coefficients matrix show that the total indirect economic loss of China's gross trade is between RMB 540.4226 billion and RMB 1023.3068 billion. Industries that are sensitive to the dispute include electrical equipment and machinery, general special equipment manufacturing, metal smelting and rolling processing, manufacture and processing of metals and metal products, and chemical. The empirical findings suggest that China establish an early-warning mechanism and trade assistance system, so that key industries that were damaged could be properly compensated.

Infrastructure Decay Modeling With the Input-Output Inoperability Model

Asset management and infrastructure interdependency concepts are found to be useful in the study of infrastructure decay. As such, infrastructure decay is modeled with the input-output inoperability model (IIM), which is a method of analysis that captures cascading effects of a disturbance in interdependent infrastructure systems. This paper presents an extension to the IIM that simplifies the construction of the interdependency matrix central to the model and integrates the use of component decay curves for each component in the system. The revised model results in the ability of infrastructure asset managers to recognize the effect of decay across an entire infrastructure network or multiple networks.

Analysis of Critical Infrastructure Network Failure in the European Union: A Combined Systems Engineering and Economic Model

Over the past few years, the European Commission has placed Critical Infrastructure Protection under the spotlight. Therefore, the Joint Research Centre is developing a tool to estimate the economic impact of Critical Infrastructure (CI) network failure, resulting from a hazard, on the regional or national level. This tool, which is presented in this study, is a combined Systems Engineering and Dynamic Inoperability Input–output model (SE-DIIM). The resilience of infrastructures and economic sectors, in terms of their ability to withstand and recover from disruptions, is included in the model. We discuss the model by analyzing the economic losses incurred in the 2003 Italian electricity network outage. The losses are estimated at both national and regional levels i.e. northern, central and southern parts of Italy and Sicily with a focus on 9 CI’s. We estimate that the economic loss for the case study under consideration is between €46 million and €173 million. We conclude that the combination of the SE and the DIIM components provides a complete framework for assessing the economic impact of critical infrastructure network failure on the national or regional level taking account of resilience.

A VULNERABILITY INDEX FOR POST-DISASTER KEY SECTOR PRIORITIZATION

Input–output-based techniques have proven to be effective in modeling how disasters lead to economic disruptions, while taking into account the structural connectivity of economic systems. In particular, through the inoperability input–output model (IIM), the degree of failure in an economic system can be quantified on a scale from 0 (normal state) to 1 (complete failure). This paper develops a vulnerability index that builds upon the foundations of the Leontief input–output model and the IIM, which is capable of identifying and prioritizing the key sectors in the aftermath of disasters. The key sector prioritization framework proposed in this paper is expected to contribute to the domain of disaster preparedness planning, such as enhancing the efficiency of resource allocation across various sectors. The proposed vulnerability index is formulated in terms of three underlying components: (1) economic impact, (2) propagation length, and (3) sector size. The vulnerability index captures the impact of investments to various sectors in times of disaster in order to yield the maximum benefits to the entire economy. This paper considers a baseline scenario that assumes that the decision-maker has an equal preference for all index components. Using Monte Carlo simulation and sensitivity analysis, we investigated the extent to which the key sector rankings could fluctuate with respect to variations in the decision-maker preferences. Key sectors tend to be sensitive to the weight assignments across the three vulnerability index components; nevertheless, some sectors are less sensitive to such weight variations and may persist on their level of priority, independent of the scenario. Using the Philippine input–output data, we found that the private services sector is consistently a high-priority sector, the trade sector is a mid-priority sector while the real estate and ownership of dwellings sector tend to be a low-priority sector.

Fuzzy Inoperability Input-output Analysis of Mandatory Biodiesel Blending Programs: The Philippine Case

Biofuels are regarded as one of the major low-carbon energy options currently available for large-scale use. Many countries have implemented biofuel programs that involve blending of bioethanol with gasoline, or biodiesel with diesel, at varying proportions. These programs are designed to address pressing concerns such as climate change, environmental quality, energy security and rural development. However, recent works suggest that biofuel resources may be at risk due to aberrant climatic events that are anticipated in the near future. Potential climate-induced disruptions include changes in precipitation levels, pest infestation, plant diseases, or increased frequency of extreme weather events. The incidence of such disruptions not only affects biofuel producers, but also energy-dependent economic sectors, resulting to “ripple effects” that further increase economic losses. We apply the inoperability input-output model (IIM) proposed by Haimes and Jiang (2001) and later enhanced by Santos and Haimes (2004) to assess the economic effects of implementing mandatory biodiesel blending programs in the Philippines. The IIM is based on the well-established technique of input-output analysis that quantifies risk through the inoperability metric, which is a dimensionless index whose value ranges from 0 (for a system functioning normally) to 1 (for a system in a state of total failure). Using the IIM, we determine the adverse effects of climate-induced biofuel disruptions on interdependent economic sectors. We estimate the resulting crop losses using the storm damage scenario from Stromberg et al. (2011) under no blending and a proposed blending rate of 5% as considered by the Department of Energy. Different ranking strategies are evaluated to determine sector vulnerability using inoperability levels, economic losses and weighted inoperability levels. For the latter scenario, equal weights are considered. Uncertainties within the modelling framework are captured through the use of fuzzy numbers.

Modelling and measuring the operability of interdependent systems and systems of systems: advances in methods and applications

Interdependency is an important consideration in managing systems and systems of systems. Included in this consideration is identifying, representing, and measuring the ripple effects of dependencies between systems and consumers who rely on their products and services. Anticipating these effects enables planners to minimise dependency risks that, if realised, can have cascading impacts on the ability of systems to deliver services. This paper presents advances in modelling interdependent systems by integrating two methods: functional dependency network analysis (FDNA) and the inoperability input-output model (IIM). Their integration enables hierarchical modelling of perturbations to systems at the physical or operational network levels. To highlight the insights gained by integrating FDNA and IIM, a simulated electric power system that feeds a large metropolitan area is presented. This simulated case demonstrates that other consequence measures, such as the inoperability metric presented herein, must be used in conjunction with monetary objectives to generate holistic prioritisation strategies.

The uncertainty recovery analysis for interdependent infrastructure systems using the dynamic inoperability input–output model

In this paper, an innovatory modelling framework is proposed to conduct the uncertainty recovery analysis for the interdependent infrastructure sectors based on the dynamic inoperability input–output model (DIIM). The DIIM captures the inoperability of infrastructure systems, and therefore can easily analyse how perturbations propagate among interconnected infrastructures and how to implement effective mitigation efforts after a disaster. In this paper, based on the random recovery time distribution, we apply the Monte Carlo simulation to obtain the distributions of the economic losses for the critical interdependent infrastructure sectors after a disaster. The proposed method can provide the decision-makers the guidance in making suitable risk-management decisions as well as how the risks can be mitigated, if the disaster cannot be avoided to happen in the first place.

Analyzing interdependent impacts of resource sustainability

Quantifying the impact of the shortage of a scarce resource requires a systemic account of the interdependent nature of several industry and infrastructure sectors that rely either directly or indirectly on that resource. An ability to quickly and easily quantify such an impact provides policymakers with a useful measure of the efficacy of discovering, designing, or developing a sustainable alternative. Discussed in this paper is a methodological approach for measuring the broader interdependent impacts of a resource shortage. The dynamic inoperability input–output model (DIIM) is used to illustrate both the economic effects of resource shortages over a period of time and the time-dependent recovery of industry sectors. Extensions to the DIIM are introduced to produce an accessible tool for policymakers and industry decision makers. Case studies using publicly available data illustrate the usefulness of the model for describing local oil production shortages and global rare earth metals supply shortages, highlighting the industries that will need to adapt to changes in resource availability, as well as those industries that will remain relatively unaffected. Above all, the model presented in this paper is an effective means of communicating the impact and importance of resource shortages to assist in the design and development of a sustainable future.

Uncertainty modeling of hurricane-based disruptions to interdependent economic and infrastructure systems

Extreme risks associated with natural and man-made disasters involve disruptions to the production of goods or provision of services in interdependent systems. The reduced supply of goods and services will degrade “as-planned” production and create ripple effects. Hence, maintaining above-minimum levels of inventory is a resilience strategy that could effectively reduce the onset of disruption. This research integrates the uncertainty in inventory levels to assess the economic impacts of moderate and extreme disastrous events on interdependent systems. The unique contribution of this research is the formulation of a stochastic inventory-based risk assessment model using a multi-objective optimization framework for minimizing (1) extreme economic losses and (2) sector inoperability. Empirical distributions are derived from inventory-to-sales ratio (ISR) of the manufacturing and trade sectors from the Bureau of Economic Analysis database. Simulations of inventory enable the initialization of inoperability functions of a dynamic inoperability input–output model (DIIM). The stochastic inventory-based DIIM-computed values of economic loss and inoperability are simultaneously minimized to identify inventory-enhancement opportunities for critically disrupted systems. A lean production case for a moderate-intensity hurricane in Virginia reveals an overestimation of regional economic loss relative to the expected inventory levels from the ISR data. The conditional expected value of regional economic loss for an extreme event is found to be $12 M higher than a moderate-intensity case. Identification of resilience-enhancement opportunities using the proposed multi-objective optimization framework could reduce expected economic loss by $24 M for an extreme-event and $17 M for a moderate-intensity case.

Multiobjective Stochastic Inoperability Decision Tree for Infrastructure Preparedness

AbstractDecision making for managing risks to critical infrastructure systems requires accounting for (1) the uncertain behavior of disruptive events; and (2) the interdependent nature of such systems that lead to large-scale inoperability. This paper integrates a dynamic risk-based interdependency model, the dynamic inoperability input-output model, with a multiobjective decision tree to analyze preparedness decisions. The use of a dynamic model allows for resilience and recovery decisions to be incorporated in the decision-making framework, and uncertainty is accounted for using probability distributions. The multiobjective inoperability decision tree is applied to the study of transportation infrastructure disruptions, namely closures of an inland waterway and an inland waterway port. A data-driven multiregional study of the Port of Catoosa in Oklahoma, along the Mississippi River Navigation System, is discussed and suggests careful consideration when investing larger amounts toward port security.

Strategic Preparedness for Recovery from Catastrophic Risks to Communities and Infrastructure Systems of Systems

Natural and human-induced disasters affect organizations in myriad ways because of the inherent interconnectedness and interdependencies among human, cyber, and physical infrastructures, but more importantly, because organizations depend on the effectiveness of people and on the leadership they provide to the organizations they serve and represent. These human-organizational-cyber-physical infrastructure entities are termed systems of systems. Given the multiple perspectives that characterize them, they cannot be modeled effectively with a single model. The focus of this article is: (i) the centrality of the states of a system in modeling; (ii) the efficacious role of shared states in modeling systems of systems, in identification, and in the meta-modeling of systems of systems; and (iii) the contributions of the above to strategic preparedness, response to, and recovery from catastrophic risk to such systems. Strategic preparedness connotes a decision-making process and its associated actions. These must be: implemented in advance of a natural or human-induced disaster, aimed at reducing consequences (e.g., recovery time, community suffering, and cost), and/or controlling their likelihood to a level considered acceptable (through the decisionmakers' implicit and explicit acceptance of various risks and tradeoffs). The inoperability input-output model (IIM), which is grounded on Leontief's input/output model, has enabled the modeling of interdependent subsystems. Two separate modeling structures are introduced. These are: phantom system models (PSM), where shared states constitute the essence of modeling coupled systems; and the IIM, where interdependencies among sectors of the economy are manifested by the Leontief matrix of technological coefficients. This article demonstrates the potential contributions of these two models to each other, and thus to more informative modeling of systems of systems schema. The contributions of shared states to this modeling and to systems identification are presented with case studies.

Risk-based input–output analysis of hurricane impacts on interdependent regional workforce systems

Natural disasters, like hurricanes, can damage properties and critical infrastructure systems, degrade economic productivity, and in extreme situations can cause injuries and mortalities. This paper focuses particularly on workforce disruptions in the aftermath of hurricanes. We extend the dynamic inoperability input–output model (DIIM) by formulating a workforce recovery model to identify critical industry sectors. A decision analysis tool is utilized by integrating the economic loss and inoperability metrics to study the interdependent effects of various hurricane intensities on Virginia’s workforce sectors. The extended DIIM and available workforce survey data are incorporated in the decision support tool to simulate various hurricane scenarios. For a low-intensity hurricane scenario, the simulated total economic loss to Virginia’s industry sectors due to workforce absenteeism is around $410 million. Examples of critical sectors that suffer the highest losses for this scenario include: (1) miscellaneous professional, scientific, and technical services; (2) federal general government; (3) state and local government enterprises; (4) construction; and (5) administrative and support services. This paper also explores the inoperability metric, which describes the proportion in which a sector capacity is disrupted. The inoperability metric reveals a different ranking of critical sectors, such as: (1) social assistance; (2) hospitals and nursing and residential care facilities; (3) educational services; (4) federal government enterprises; and (5) federal general government. Results of the study will help identify the critical workforce sectors and can ultimately provide insights into formulating preparedness decisions to expedite disaster recovery. The model was applied to the state of Virginia but can be generalized to other regions and other disaster scenarios.

Empirical Data and Regression Analysis for Estimation of Infrastructure Resilience with Application to Electric Power Outages

Recent natural disasters have highlighted the need for increased planning for disruptive events. Forecasting damage and time that a system will be inoperable is important for disruption planning. The resilience of critical infrastructure systems, or their ability to recover quickly from a disruption, can mitigate adverse consequences of the disruption. This paper quantifies the resilience of a critical infrastructure sector through the dynamic inoperability input-output model (DIIM). The DIIM, which describes how inoperability propagates through a set of interdependent industry and infrastructure sectors following a disruptive event, includes a resilience parameter that has not yet been adequately assessed. This paper provides a data-driven approach to derive the resilience parameter through regression models. Data may contain different disruption scenarios, and regression models can incorporate these scenarios through the use of categorical or dummy variables. A mixed-effects model offers an alternate approach of accounting for these scenarios, and these models estimate parameters based on the combination of all scenarios (fixed effects) and an individual scenario (random effects). These regression models are illustrated with electric power outage data and a regional disruption that uses the DIIM to model production losses in Oklahoma following an electric power outage.

Time-Varying Input-Output Inoperability Model

AbstractRepresenting interdependent critical infrastructures is mandatory for implementing protection policies and strategies. Among the several interdependency models provided over the years, the input-output inoperability model (IIM) has attracted widespread attention due to its simplicity and compactness. Such a model can emphasize cascading effects induced in a complex scenario by dependencies and interdependencies; however, the model is typically set up based on economic data, and the stationary assumption greatly reduces the applicability of the framework. These aspects are crude approximations due to the intrinsic limits of the methodology. Indeed, in modeling realistic scenarios, the coupling of different infrastructures and sectors is expected to increase with outage duration. In this paper, a different formulation of the IIM is proposed where time-varying interdependency coefficients are considered. Such coefficients are defined to explicitly account for the severity and duration of negative phe...

An uncertainty assessment of interdependent infrastructure systems and infrastructure sectors with natural disasters analysis

In this paper, an uncertainty assessment analysis extension is proposed for the inoperability input-output model (IIM), which is a methodology for assessing the propagated consequences of initial disruptions to a set of sectors. The IIM has been developed to help understand infrastructure interdependencies on deliberate external attacks or unfortunate natural disasters. Since the final demand perturbation may be affected by the psychological impact on consumers. In this paper, the uncertainty demand-driven IIM is extended to allow final demand perturbations in the form of lognormal probability distributions. The Monte Carlo simulation is used to demonstrate the uncertainty analysis methodology in detail through a four-sector economy system, and the results show that the value of interdependency analysis in the risk assessment process as an integral part of interdependent infrastructure.

From the Editors

From the Editors