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Urban water distribution systems hold a critical and strategic position inpreserving public health and industrial growth. Despite the ubiquity of theseurban systems, aging infrastructure, and increased risk of terrorism, decisionsupport models for a timely and adaptive contamination emergency response stillremain at an undeveloped stage. Emergency response is characterized as aprogressive, interactive, and adaptive process that involves parallelactivities of processing streaming information and executing response actions.This study develops a dynamic decision support model that adaptively simulatesthe time-varying emergency environment and tracks changing best healthprotection response measures at every stage of an emergency in real-time.Feedback mechanisms between the contaminated network, emergency managers, andconsumers are incorporated in a dynamic simulation model to capturetime-varying characteristics of an emergency environment. Anevolutionary-computation-based dynamic optimization model is developed toadaptively identify time-dependant optimal health protection measures during anemergency. This dynamic simulation-optimization model treats perceivedcontaminant source attributes as time-varying parameters to account forperceived contamination source updates as more data stream in over time.Performance of the developed dynamic decision support model is analyzed anddemonstrated using a mid-size virtual city that resembles the dynamics andcomplexity of real-world urban systems. This adaptive emergency responseoptimization model is intended to be a major component of an all-inclusivecyberinfrastructure for efficient contamination threat management, which iscurrently under development.

A Dynamic Simulation-Optimization Model for Adaptive Management of Urban Water Distribution System Contamination Threats