Multiobjective Stochastic Inoperability Decision Tree for Infrastructure Preparedness

Abstract

Decision making for managing risks to critical infrastructure systems requires accounting for (1) the uncertain behavior of disruptive events; and (2) the interdependent nature of such systems that lead to large-scale inoperability. This paper integrates a dynamic risk-based interdependency model, the dynamic inoperability input-output model, with a multiobjective decision tree to analyze preparedness decisions. The use of a dynamic model allows for resilience and recovery decisions to be incorporated in the decision-making framework, and uncertainty is accounted for using probability distributions. The multiobjective inoperability decision tree is applied to the study of transportation infrastructure disruptions, namely closures of an inland waterway and an inland waterway port. A data-driven multiregional study of the Port of Catoosa in Oklahoma, along the Mississippi River Navigation System, is discussed and suggests careful consideration when investing larger amounts toward port security.