Kash Barker

A review of definitions and measures of system resilience

Modeling and evaluating the resilience of systems, potentially complex and large-scale in nature, has recently raised significant interest among both practitioners and researchers. This recent interest has resulted in several definitions of the concept of resilience and several approaches to measuring this concept, across several application domains. As such, this paper presents a review of recent research articles related to defining and quantifying resilience in various disciplines, with a focus on engineering systems. We provide a classification scheme to the approaches in the literature, focusing on qualitative and quantitative approaches and their subcategories. Addressed in this review are: an extensive coverage of the literature, an exploration of current gaps and challenges, and several directions for future research.

Resilience-based network component importance measures

Disruptive events, whether malevolent attacks, natural disasters, manmade accidents, or common failures, can have significant widespread impacts when they lead to the failure of network components and ultimately the larger network itself. An important consideration in the behavior of a network following disruptive events is its resilience, or the ability of the network to “bounce back†to a desired performance state. Building on the extensive reliability engineering literature on measuring component importance, or the extent to which individual network components contribute to network reliability, this paper provides two resilience-based component importance measures. The two measures quantify the (i) potential adverse impact on system resilience from a disruption affecting link i, and (ii) potential positive impact on system resilience when link i cannot be disrupted, respectively. The resilience-based component importance measures, and an algorithm to perform stochastic ordering of network components due to the uncertain nature of network disruptions, are illustrated with a 20 node, 30 link network example.

Assessing uncertainty in extreme events: Applications to risk-based decision making in interdependent infrastructure sectors

Abstract Risk-based decision making often relies upon expert probability assessments, particularly in the consequences of disruptive events and when such events are extreme or catastrophic in nature. Naturally, such expert-elicited probability distributions can be fraught with errors, as they describe events which occur very infrequently and for which only sparse data exist. This paper presents a quantitative framework, the extreme event uncertainty sensitivity impact method (EE-USIM), for measuring the sensitivity of extreme event consequences to uncertainties in the parameters of the underlying probability distribution. The EE-USIM is demonstrated with the Inoperability input–output model (IIM), a model with which to evaluate the propagation of inoperability throughout an interdependent set of economic and infrastructure sectors. The EE-USIM also makes use of a two-sided power distribution function generated by expert elicitation of extreme event consequences.

Static and dynamic metrics of economic resilience for interdependent infrastructure and industry sectors

Abstract Infrastructures are needed for maintaining functionality and stability of society, while being put under substantial stresses from natural or man-made shocks. Since avoiding shock is impossible, increased focus is given to infrastructure resilience, which denotes the ability to recover and operate under new stable regimes. This paper addresses the problem of estimating, quantifying and planning for economic resilience of interdependent infrastructures, where interconnectedness adds to problem complexity. The risk-based economic input–output model enterprise, a useful tool for measuring the cascading effects of interdependent failures, is employed to introduce a framework for economic resilience estimation. We propose static and dynamic measures for resilience that confirm to well-known resilience concepts of robustness, rapidity, redundancy, and resourcefulness. The quantitative metrics proposed here (static resilience metric, time averaged level of operability, maximum loss of functionality, time to recovery) guide a preparedness decision making framework to promote interdependent economic resilience estimation. Using the metrics we introduce new multi-dimensional resilience functions that allow multiple resource allocation scenarios. Through an example problem we demonstrate the usefulness of these functions in guiding resource planning for building resilience.

A Risk-Based Approach for Identifying Key Economic and Infrastructure Systems

This article introduces approaches for identifying key interdependent infrastructure sectors based on the inventory dynamic inoperability input-output model, which integrates an inventory model and a risk-based interdependency model. An identification of such key sectors narrows a policymakers focus on sectors providing most impact and receiving most impact from inventory-caused delays in inoperability resulting from disruptive events. A case study illustrates the practical insights of the key sector approaches derived from a value of workforce-centered production inoperability from Bureau of Economic Analysis data.

Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows

Given the ubiquitous nature of infrastructure networks in todays society, there is a global need to understand, quantify, and plan for the resilience of these networks to disruptions. This work defines network resilience along dimensions of reliability, vulnerability, survivability, and recoverability, and quantifies network resilience as a function of component and network performance. The treatment of vulnerability and recoverability as random variables leads to stochastic measures of resilience, including time to total system restoration, time to full system service resilience, and time to a specific α% resilience. Ultimately, a means to optimize network resilience strategies is discussed, primarily through an adaption of the Copeland Score for nonparametric stochastic ranking. The measures of resilience and optimization techniques are applied to inland waterway networks, an important mode in the larger multimodal transportation network upon which we rely for the flow of commodities. We provide a case study analyzing and planning for the resilience of commodity flows along the Mississippi River Navigation System to illustrate the usefulness of the proposed metrics.

Modeling infrastructure resilience using Bayesian networks

We discuss the use of Bayesian networks to calculate resilience.We model resilience from absorptive, adaptive, and restorative capacity perspectives.We study the resilience of an inland waterway network example. Infrastructure systems, including transportation, telecommunications, water supply, and electric power networks, are faced with growing number of disruptions such as natural disasters, malevolent attacks, human-made accidents, and common failures, due to their age, condition, and interdependence with other infrastructures. Risk planners, previously concerned with protection and prevention, are now more interested in the ability of such infrastructures to withstand and recover from disruptions in the form of resilience building strategies. This paper offers a means to quantify resilience as a function of absorptive, adaptive, and restorative capacities with Bayesian networks. A popular tool to structure relationships among several variables, the Bayesian network model allows for the analysis of different resilience building strategies through forward and backward propagation. The use of Bayesian networks to quantify resilience is demonstrated with the example of an inland waterway port, an important component in the intermodal transportation network.

Sequential Decision-making in Interdependent Sectors with Multiobjective Inoperability Decision Trees: Application to Biofuel Subsidy Analysis

Abstract Decision-making involving large-scale systems often involves considerations for temporal changes, interdependencies in organizational structures, multiple competing objectives, and risk and uncertainty, among others. In this paper we develop a risk-based methodology, the Multiobjective Inoperability Decision Tree (MOIDT). It integrates several dimensions of decision-making processes associated with interconnected systems in terms of: (i) evaluation of sequential policies; (ii) analysis of interdependencies; (iii) treatment of multiple objectives and their tradeoffs; and (iv) characterization of uncertainties. To demonstrate the integration of these four components, we present a case study to analyze the impact of government policies towards mass-scale biofuel production. Using a multi-period decision framework, the analysis utilizes economic input–output data to model the probabilistic demand adjustments for sectors that will likely be affected by biofuel policies.

Learning from student data

An abundance of information is contained on every college campus. Many academic, demographic, and attitudinal variables are gathered for every student who steps on campus. Despite all this information, colleges still struggle with graduation rates. This is an apt example of an overload of information but a starvation of knowledge. This paper introduces the use of neural networks and support vector machines, both nonlinear discriminant methods, for classifying student graduation behavior from several academic, demographic, and attitudinal variables maintained about students at the University of Oklahoma

Flow-based vulnerability measures for network component importance: Experimentation with preparedness planning

This work develops and compares several flow-based vulnerability measures to prioritize important network edges for the implementation of preparedness options. These network vulnerability measures quantify different characteristics and perspectives on enabling maximum flow, creating bottlenecks, and partitioning into cutsets, among others. The efficacy of these vulnerability measures to motivate preparedness options against experimental geographically located disruption simulations is measured. Results suggest that a weighted flow capacity rate, which accounts for both (i) the contribution of an edge to maximum network flow and (ii) the extent to which the edge is a bottleneck in the network, shows most promise across four instances of varying network sizes and densities.