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This thesis presents three novel contributions to the application as well as development of ranking and selection procedures. Ranking and selection is an important topic in the discrete event simulation literature concerned with the use of statistical approaches to select the best or set of best systems from a set of simulated alternatives. Ranking and selection is comprised of three different approaches: subset selection, indifference zone selection, and multiple comparisons. The methodology addressed in this thesis focuses primarily on the first two approaches: subset selection and indifference zone selection.
Our first contribution regards the application of existing ranking and selection procedures to an important body of literature known as system reliability design. If we are capable of modeling a system via a network of arcs and nodes, then the difficult problem of determining the most reliable network configuration, given a set of design constraints, is an optimization problem that we refer to as the network reliability design problem. In this thesis, we first present a novel solution approach for one type of network reliability design optimization problem where total enumeration of the solution space is feasible and desirable. This approach focuses on improving the efficiency of the evaluation of system reliabilities as well as quantifying the probability of correctly selecting the true best design based on the estimation of the expected system reliabilities through the use of ranking and selection procedures, both of which are novel ideas in the system reliability design literature. Altogether, this method eliminates the guess work that was previously associated with this design problem and maintains significant runtime improvements over the existing methodology.
Our second contribution regards the development of a new optimization framework for the network reliability design problem that is applicable to any topological and terminal configuration as well as solution sets of any sizes. This framework focuses on improving the efficiency of the evaluation and comparison of system reliabilities, while providing a more robust performance and user-friendly procedure in terms of the input parameter level selection. This is accomplished through the introduction of two novel statistical sampling procedures based on the concepts of ranking and selection: Sequential Selection of the Best Subset and Duplicate Generation. Altogether, this framework achieves the same convergence and solution quality as the baseline cross-entropy approach, but achieves runtime and sample size improvements on the order of 450% to 1500% over the example networks tested.
Our final contribution regards the development and extension of the general ranking and selection literature with novel procedures for the problem concerned with the selection of the -best systems, where system means and variances are unknown and potentially unequal. We present three new ranking and selection procedures: a subset selection procedure, an indifference zone selection procedure, and a combined two stage subset selection and indifference zone selection procedure. All procedures are backed by proofs of the theoretical guarantees as well as empirical results on the probability of correct selection. We also investigate the effect of various parameters on each procedure's overall performance.