Publications

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Decision makers increasingly rely on large-scale computational models to simulate and analyze complex man-made systems. For example, computational models of national infrastructures are being used to inform government policy, assess economic and national security risks, evaluate infrastructure interdependencies, and plan for the growth and evolution of infrastructure capabilities. A major challenge for decision makers is the analysis of national-scale models that are composed of interacting systems: effective integration of system models is difficult, there are many parameters to analyze in these systems, and fundamental modeling uncertainties complicate analysis. This project is developing optimization methods to effectively represent and analyze large-scale heterogeneous system of systems (HSoS) models, which have emerged as a promising approach for describing such complex man-made systems. These optimization methods enable decision makers to predict future system behavior, manage system risk, assess tradeoffs between system criteria, and identify critical modeling uncertainties.

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We consider the design of a sensor network to serve as an early warning system against a potential suite of contamination incidents. Given any measure for evaluating the quality of a sensor placement, there are two ways to model the objective. One is to minimize the impact or damage to the network, the other is to maximize the reduction in impact compared to the network without sensors. These objectives are the same when the problem is solved optimally. But when given equally-good approximation algorithms for each of this pair of complementary objectives, either one might be a better choice. The choice generally depends upon the quality of the approximation algorithms, the impact when there are no sensors, and the number of sensors available. We examine when each objective is better than the other by examining multiple real world networks. When assuming perfect sensors, minimizing impact is frequently superior for virulent contaminants. But when there are long response delays, or it is very difficult to reduce impact, maximizing impact reduction may be better. © 2011 ASCE.

A commonly used indicator of water quality is the amount of residual chlorine in a water distribution system. Chlorine booster stations are often utilized to maintain acceptable levels of residual chlorine throughout the network. In addition, hyper-chlorination has been used to disinfect portions of the distribution system following a pipe break. Consequently, it is natural to use hyper-chlorination via multiple booster stations located throughout a network to mitigate consequences and decontaminate networks after a contamination event. Many researchers have explored different methodologies for optimally locating booster stations in the network for daily operations. In this research, the problem of optimally locating chlorine booster stations to decontaminate following a contamination incident will be described. © 2011 ASCE.

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