Evaluating information assurance strategies
Decision Support Systems
A bilevel mixed-integer program for critical infrastructure protection planning
Computers and Operations Research
Government preparedness: Using simulation to prepare for a terrorist attack
Computers and Operations Research
Decision support for network disruption mitigation
Decision Support Systems
Real-time supply chain control via multi-agent adjustable autonomy
Computers and Operations Research
Computers and Operations Research
A two-stage stochastic programming model for transportation network protection
Computers and Operations Research
The Crutial Way of Critical Infrastructure Protection
IEEE Security and Privacy
Pre-disaster investment decisions for strengthening a highway network
Computers and Operations Research
A multicriteria decision support system for bank rating
Decision Support Systems
Representing perceived tradeoffs in defining disaster resilience
Decision Support Systems
Multi-expert operational risk management
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper presents an approach for providing a quantitative measure of resilience in the presence of multiple related disaster events. It extends the concepts of the resilience triangle and predicted disaster resilience by considering the tradeoffs between multiple criteria for each individual sub-event, as well as for an entire multi-event situation. The focus of the research is on sudden-onset disasters, and on the initial impact of each sub-event as well as the amount of time available to work towards recovery of the system before the next sub-event occurs. A mathematical model is developed for the new resilience measure, along with an approach for graphically representing the relationships between the different criteria. An example is then provided of using the new approach to compare the relative resilience of different scenarios under a representative multi-event disaster situation. The results demonstrate that characterizing multi-event resilience analytically can ultimately provide a great depth of information and thus support better disaster planning and mitigation.