Grant: $311,473 - National Science Foundation - Jul. 16, 2009
0% voted satisfied - 100% voted not satisfied - 4 vote(s) cast
Award Description: The proposed research is addressing the important question of the impact of human intervention on uncertainties that drive a particular system. Methods will be developed to assess whether interactions between decisions and uncertainties exist. If the answer is affirmative a new stochastic process equipped with 'decision-dependent-prior distributions' is proposed. New models and Markov Chain Monte Carlo estimation procedures are defined to enable Bayesian inference for decision-dependent models. The second important question addressed is how to make optimal decisions in such environments. It has been approached by several researchers but with limited advances. This proposal provides a new procedure for tackling these very difficult optimization problems. The proposed methods and algorithms will be applied to evaluate and construct maintenance system at nuclear power plants. Properly accounting for and quantifying uncertainty in maintenance policy decisions will reduce the variability in nuclear power production of electricity, while providing utility engineers quantified assessments of equipment performance on safety. Properly understanding and modeling the interplay between decisions and uncertainties, and their impact on the stochastic optimization problem at hand will enable utility engineers to better operate their power plants, while, simultaneously, minimizing costs. The potential impact is not limited to the electric power industry. In fact, the results will be applicable to any system in which future stochastic behavior is influenced by current and past human decisions. There are a plethora of real world applications that could merit from understanding and modeling decision-dependency along the lines of the research detailed in this proposal.
Project Description: During the first two months of the project we initiated work on the first decision-dependent model described in the proposal. One of the main accomplishments is the gathering, coding, and construction of a large data set using information provided by the Risk management group at South Texas Project Nuclear Operating Company. The data set includes: time to failure and type of maintenance intervention. We have one publication accepted and will appear in the Proceedings of the 2009 Winter Simulation Conference in Austin Texas. The citation is: Belyi, D., P. Damien, D. Morton, and E. Popova, ?Bayesian non-parametric simulation of hazard functions?, Proceedings of the 2009 Winter Simulation Conference, M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R. G. Ingalls (eds.), December, 2009.
Jobs Summary: A Graduate Research Assistant was appointed .24 FTE. Number of Jobs was calculated using OMB guidance. (Total jobs reported: 0)
Project Status: Less Than 50% Completed
This award's data was last updated on Jul. 16, 2009. Help expand these official descriptions using the wiki below.