Health Care System Disaster-Resilience Optimization Given Its Reliance on Interdependent Critical Lifelines

Abstract

Natural or human-made incidents in urban areas cause surges in demand for hospitals while often simultaneously limiting their capacity to serve patients due to direct physical damage or service interruptions in supporting lifelines. A hierarchical modeling concept is proposed to quantify the resilience of regional hospital response under disaster. To facilitate the optimization of pre- and postevent resilience-enhancing actions for a range of potential hazard-demand-damage scenarios, a multistage stochastic, mixed-integer decision problem (SMIP) is proposed. Dependencies on interdependent, potentially damaged critical lifelines are explicitly modeled. Resilience is estimated in terms of total patient waiting time and unserved patients. These quantities are captured in the objective of the SMIP through the adoption of a simulation-based metamodel of a detailed hospital model. The SMIP is demonstrated in a numerical example developed to represent the main hospitals of the Johns Hopkins Health System Corporation. The proposed structure is broadly applicable to other societal functions that take place in interrelated buildings connected by transportation and communications links and are dependent on interdependent lifeline networks.