Date of Award
Master of Science in Operations Research
Department of Operational Sciences
Jeffery D. Weir, PhD
Portfolio selection problems with combinatorially-large alternative sets can be impossible to evaluate precisely on a reasonable timescale. When portfolios require complex modeling for performance assessment, prohibitive computational processing times can result. Eliminating a small number of alternatives through an intelligent screening process can greatly reduce the number of alternative combinations, thereby decreasing a problem's evaluation time and cost. A methodology was developed for the class of hierarchical portfolio selection problems in which multiple objectives are all judged on the same sub-objectives. First, a novel capability-based alternative screening process was devised to identify and remove poor alternatives, thereby reducing the number of portfolios. Then, a performance-based portfolio screening process was explored to estimate portfolio sufficiency according to the performance requirements of the decision maker. Following the establishment of a set of sufficient portfolios, the analyst can employ higher resolution post-analysis methods to choose a final solution. Finally, the methodology was applied to a portfolio selection problem in which the United States Strategic Command attempts to select an ideal mix of intelligence, surveillance, and reconnaissance assets. After deconstructing the actual objective hierarchy, a set of representative alternatives were evaluated and a variety of screening procedures were applied to demonstrate significant reduction in the number of possible portfolios.
DTIC Accession Number
Cote, Michael D., "Screening and Sufficiency in Multiobjective Decision Problems with Large Alternative Sets" (2010). Theses and Dissertations. 2101.