Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows

Abstract

Given the ubiquitous nature of infrastructure networks in today's 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.

Framework for improving the resilience and recovery of transportation networks under geohazard risks

“…The speed of recovery, or so-called "rapidity", has been the focus of evaluations for system resilience in the literature, as described by Ganin et al [19]. This concept, first proposed by Bruneau et al [6], has since then been modified to investigate specifically the "rapidity" dimension of resilience as it refers to the speed of recovery [14,21,4].…”

Modelo de aproximación lineal para la medición de resiliencia en cadenas de suministro

“…En otra línea de medición de resiliencia se definen los estados del sistema antes, durante y después de un evento disruptivo, con el fin elaborar un perfil más amplio de un sistema con una modelación del proceso de interrupción y recuperación ajustados en una mayor proporción a la realidad [27][28][29][30][31].…”

Allocating Resources to Enhance Resilience, with Application to Superstorm Sandy and an Electric Utility

“…(19) Incorporating uncertainty with this metric produces a stochastic measure of resilience, and the optimal recovery activity can be identified via simulation (20) or by comparing the percentiles of different probability distributions of resilience generated by different recovery activities. (21) Although the motivation behind measuring resilience typically focuses on helping decision makers prepare and respond to disruptions, less research has concentrated on how resources should be allocated to improve an organization or system's resilience. The literature studying how to optimize system resilience usually defines activities to pursue or components to protect and then uses a numerical method such as simulation to select the best alternative.…”

“…The literature studying how to optimize system resilience usually defines activities to pursue or components to protect and then uses a numerical method such as simulation to select the best alternative. (19,20,21) In a real-world context, however, it is important to keep in mind that there are limits not only on how quickly a system can recover from a disruption, but also on the physical and financial resources available to help offset the disaster's impacts. A process manager will thus need to focus on determining the most effective way to allocate his or her resources in order to improve system resilience.…”