Representing perceived tradeoffs in defining disaster resilience

Lean, green and resilient practices influence on supply chain performance: interpretive structural modeling approach

“…Resilience refers to the ability of the supply chain to cope with unexpected disturbances. In supply chain systems, the objective is to react efficiently to the negative effects of disturbances (which could be more or less severe) (Zobel 2011). The aim of the resilient strategies has two manifolds (Haimes 2006): (1) to recover the desired values of the states of a system that has been disturbed, within an acceptable time period and at an acceptable cost; and (2) to reduce the effectiveness of the disturbance by changing the level of the effectiveness of a potential threat.…”

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

“…The dummy variables and the mixed effects model each incorporate individual scenario disruptions and measure the extent to which the industry resilience parameter changes for different scenarios. Both the intercept and slope describe the relationship between the initial impact and recovery time, often referred to as robustness and rapidity in the resilience literature (Bruneau et al 2003;Zobel 2011). The intercept changes when the model includes different power outage scenarios, and we conclude that the power outage scenario impacts the relationship between the initial impact and recovery time.…”

“…Bruneau et al (2003) quantify resilience by measuring the expected loss due to an earthquake over time. Zobel (2011) builds on this resilience metric by relating recovery time to the initial impact and analyzing how preparedness efforts might need to trade off reducing the initial impact with reducing recovery time. Other approaches to quantifying resilience include comparing the estimated loss in economic output with the maximum predicted loss in output (Rose 2007), developing an influence or decision diagram that incorporates pre-disruption and post-disruption decisions (McDaniels et al 2008), calculating the ability of a node to remain connected to a degraded network (Dueñas-Osorio et al 2004), and combining the ability of a network to provide service with the time to restore service when failure occurs (Whitson and Ramirez-Marquez 2009).…”

Decision support for long-range, community-based planning to mitigate against and recover from potential multiple disasters

“…One particular type of measure that has been used to represent an entity's capacity for resisting loss and then recovering over time has been resilience (Zobel, 2011;Zobel & Khansa, 2014). Resilience, as a measure, can provide a more complete picture of the community's viability over the long-term by extending the descriptive abilities of short-term emergency related measures such as deprivation costs.…”

“…To measure the resilience of q(t) over the study horizon [0,T*], we adopt the engineering-based approach of calculating resilience to be the area under q(t) as a percentage of the total area Q* available if no loss or outside influence on q occurs (See Bruneau et al, 2003;Zobel, 2011;Zobel & Khansa, 2012):…”