This grant provides funding for the development of new methodologies and supporting theory for robust modeling and analysis of large-scale stochastic discrete-event dynamic systems using mathematical programming. The research will involve robust modeling and sensitivity analysis of discrete-event systems as well as structural property analyses of system dynamics. The work will combine simulation and mathematical programming approaches to get more efficient simulation models, predict logical errors in simulation models, and obtain more robust simulation results. Discrete-event system dynamics will be modeled analytically to support sensitivity analyses for simulation when traditional methods fail. This will enable modelers to extract more information from a single simulation run, make effective use of partial information for non-simulated experiments and support the application of real-time sensitivity information for large-scale systems. Large-scale, real-world systems will provide a test bed for implementation of the concepts and methodologies developed through the project. Multidisciplinary research opportunities for undergraduate and graduate students will be an important outcome of this project which features an integrated educational plan leading to course restructuring, mentorship activities, and K-12 outreach. Significant benefits are anticipated from this research. First, new math-programming-based approaches to achieving model efficiency, error reduction, and robust experiments for discrete-event simulation in the presence of input uncertainties and/or model errors will be developed. Second, the work will lead to an improved understanding of simulation system dynamics and analytical methodologies for analyzing them. Third, experimenters will be able to extract more information from a single simulation run and apply robust sensitivity analysis procedures. Ultimately, new math-programming-based simulation methodologies for modeling and analysis of discrete-event simulation will improve the effectiveness and efficiency of the systems modeling process for a wide range of practical applications.
