Sequential Procedures for the ”Selection” Problems in Discrete Simulation Optimization
Abstract
The simulation optimization problems refer to the nonlinear optimization problems whose objective function can be evaluated through stochastic simulations. We study two significant discrete simulation optimization problems in this thesis: Ranking and Selection (R&S) and Factor Screening (FS). Both R&S and FS are the ”selection” problems defined upon a finite set of candidate systems or factors. They vary mainly in their objectives: the R&S problems is to find the ”best” system(s) among all alternatives; whereas the FS is to select important factors that are critical to the stochastic systems. In this thesis, we develop efficient sequential procedures for these two problems. For the R&S problem, we propose fully-sequential procedures for selecting the ”best” systems with a guaranteed probability of correct selection (PCS). The main features of the stated methods are: (1) a Bonferroni-free model, these procedures overcome the conservativeness of the Bonferroni correction and deliver the exact probabilistic guarantee without overshooting; (2) asymptotic optimality, these procedures achieve the lower bound of average sample size asymptotically; (3) an indifference-zone-flexible formulation, these procedures bridge the gap between the indifference-zone formulation and the indifference-zone-free formulation so that the indifference-zone parameter is not indispensable but could be helpful if provided. We establish the validity and asymptotic efficiency for the proposed procedure and conduct numerical studies to investigates the performance under multiple configurations.
Degree
Ph.D.
Advisors
Wan, Purdue University.
Subject Area
Statistics|Management|Operations research
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