Multiple Comparisons for Optimization via Simulation

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The research will focus on derivations of statistically efficient multiple-comparison procedures for optimization via stochastic simulation. The results will be applicable when the objective is to maximize or minimize expected system performance with respect to a discrete, finite decision space. Such problems are encountered when a number of alternative system designs arise from combinations of equipment, schedules or facilities, subject to constraints on the available budget or technology. Multiple- comparison procedures are the natural approach for optimization in such design problems. Two major efforts concentrate on basic methods for multiple comparisons: (1) multiple-comparison inference, and thus be able to distinguish small differences in expected system performance and (2) derivations of multiple- comparison procedures for steady-state simulation experiements. Results of this research should aid in making Monte Carlo simulation, a most important tool for analysis of complex systems, more efficient and useful.