Ranking and Selection procedures have been designed to select the best system from a
number of alternatives, where the best system is defined by the given problem. The primary
focus of this thesis is on experiments where the data are from simulated systems. In simulation
ranking and selection procedures, four classes of comparison problems are typically
encountered. We focus on two of them: Bernoulli and multinomial selection. Therefore, we
wish to select the best system from a number of simulated alternatives where the best system
is defined as either the one with the largest probability of success (Bernoulli selection)
or the one with the greatest probability of being the best performer (multinomial selection).
We focus on procedures that are sequential and use an indifference-zone formulation
wherein the user specifies the smallest practical difference he wishes to detect between the
best system and other contenders.
We apply fully sequential procedures due to Kim and Nelson (2004) to Bernoulli data
for terminating simulations, employing common random numbers. We find that significant
savings in total observations can be realized for two to five systems when we wish to detect
small differences between competing systems. We also study the multinomial selection
problem. We offer a Monte Carlo simulation of the Bechhofer and Kulkarni (1984) MBK
multinomial procedure and provide extended tables of results. In addition, we introduce a
multi-factor extension of the MBK procedure. This procedure allows for multiple independent
factors of interest to be tested simultaneously from one data source (e.g., one person
will answer multiple independent surveys) with significant savings in total observations
compared to the factors being tested in independent experiments (each survey is run with
separate focus groups and results are combined after the experiment). Another multi-factor
multinomial procedure is also introduced, which is an extension to the MBG procedure due to Bechhofer and Goldsman (1985, 1986). This procedure performs better that any other
procedure to date for the multi-factor multinomial selection problem and should always be
used whenever table values for the truncation point are available.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/7603 |
| Date | 02 December 2004 |
| Creators | Malone, Gwendolyn Joy |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 1343150 bytes, application/pdf |
Page generated in 0.0018 seconds