Inoperability Model Research Articles

Analysis of the susceptibility of interdependent infrastructures using fuzzy input–output inoperability model: the case of flood hazards in Tehran

Advancement in technology has contributed to increment in complexity of systems and infrastructures. Furthermore, it has complicated the management of systems to deal with natural hazards. Input–output inoperability model (IIM) is a simple method to characterize the impacts of natural hazards on interconnected infrastructures. In this paper, the impacts of a flood hazard on six critical infrastructures in Tehran metropolitan have been assessed by using IIM. The computational results show that energy and transportation infrastructures are the most influencing infrastructures, while emergency services and healthcare infrastructures are the most influenced infrastructures. All data required to evaluate this case study have been collected using questionnaires and converted to fuzzy interdependency values. To increase decision-making power, the developed fuzzy matrix has been arranged for different risk levels (from absolutely optimistic to absolutely pessimistic) and confidence levels (from absolutely confident to absolutely non-confident). Afterward, the interdependency matrix has been deffuzified, and inoperability of infrastructures has been calculated by the IIM for seven different initial conditions. Finally, a sensitivity analysis has been conducted to incorporate the risk levels and confidence levels to determine values of inoperability under the above-mentioned conditions. The ranking for both of the influencing and the influenced infrastructures has also been provided. This ranking helps decision makers to manage natural hazard risks effectively by appropriate resource allocation. It also helps to realize the interdependencies among infrastructures and to determine the inoperability of infrastructures before natural hazards. This would help decision makers to mitigate the risk and prepare the society well in advance.

Fuzzy dynamic input–output inoperability model

This paper describes an extension of the input–output inoperability model (IIM) that accommodates uncertain and vague data. In the resulting “fuzzy version” of the dynamic IIM model (FD-IIM), the level of inoperability of each infrastructure and the Leontief coefficients are specified using fuzzy variables that express expert knowledge about infrastructure interdependences. An important result is that, under certain conditions, solution convergence for the fuzzy model can be inferred based on the stability properties of the “crisp” (non-fuzzy) version of the system of interest. A case study involving the Italian critical infrastructure is used to demonstrate the feasibility and utility of the approach.

Critical infrastructure dependency assessment using the input–output inoperability model

The input–output inoperability model (IIM) is a simple, but powerful, mechanism for analyzing the cascading effects induced by critical infrastructure dependencies and interdependencies. IIM typically uses financial data as a measure of the dependency phenomena. Since financial data is only one of the many dimensions for analyzing dependency phenomena, the quality of IIM parameters and, thus, the reliability of IIM results can be affected negatively. This paper proposes a methodology for evaluating IIM parameters based on technical and operational data. The data is collected by interviewing experts and is processed using a fuzzy set based methodology. A case study involving Italian critical infrastructure sectors is used to demonstrate the effectiveness of the methodology.

Multi‐period model for disruptive events in interdependent systems

AbstractThis paper develops the Multi‐Period Model for Disruptive Events in Interdependent Systems (MPMDEIS). The analysis is divided into two principal periods: (1) an event period that lasts for the duration of the active perturbation and results in the degradation of the interdependent systems, and (2) a recovery period during which the systems evolves to a new equilibrium. The event period perturbation is modeled as a shock that cascades among the interdependent systems and reduces their production capacity. The state of the systems at the end of the event period is specified by its operational unavailability vector, which is given by the initial perturbation strength vector multiplied by an exponential matrix with the interdependency matrix P as the exponent. The coefficients of P represent the relative contribution of the loss of capacity of one system to the loss of another system as the perturbation propagates from one to the other. The quantification of the shock and interdependencies is critical for the realistic assessment of the inflicted damage and formulating effective defensive strategies against terrorist attacks, natural hazards, and technological failures or accidents. The recovery period depends on the implemented response actions and it can be modeled using a systems dynamics approach. The standard economic input‐output tables are unlikely to be appropriate for the events of interest. However, there are probabilistic risk analysis techniques such as eliciting expert opinions and analytic methods which can be used to reliably assess these data. The MPMDEIS is compared with the Inoperability Input‐Output Model (IIM) and Dynamic Input‐Output Inoperability Model (DIIM) and it is demonstrated to avoid IIM and DIIM assumptions that can lead to significant errors for disruptive events in interdependent systems. © 2006 Wiley Periodicals, Inc. Syst Eng 9: 281– 295, 2006