  Urban water distribution systems hold a critical and strategic position in
preserving public health and industrial growth. Despite the ubiquity of these
urban systems, aging infrastructure, and increased risk of terrorism, decision
support models for a timely and adaptive contamination emergency response still
remain at an undeveloped stage. Emergency response is characterized as a
progressive, interactive, and adaptive process that involves parallel
activities of processing streaming information and executing response actions.
This study develops a dynamic decision support model that adaptively simulates
the time-varying emergency environment and tracks changing best health
protection response measures at every stage of an emergency in real-time.
Feedback mechanisms between the contaminated network, emergency managers, and
consumers are incorporated in a dynamic simulation model to capture
time-varying characteristics of an emergency environment. An
evolutionary-computation-based dynamic optimization model is developed to
adaptively identify time-dependant optimal health protection measures during an
emergency. This dynamic simulation-optimization model treats perceived
contaminant source attributes as time-varying parameters to account for
perceived contamination source updates as more data stream in over time.
Performance of the developed dynamic decision support model is analyzed and
demonstrated using a mid-size virtual city that resembles the dynamics and
complexity of real-world urban systems. This adaptive emergency response
optimization model is intended to be a major component of an all-inclusive
cyberinfrastructure for efficient contamination threat management, which is
currently under development.
