Date of Award
3-2020
Document Type
Thesis
Degree Name
Master of Science in Operations Research
Department
Department of Operational Sciences
First Advisor
Beau A. Nunnaly, PhD
Abstract
Current research provides a method to incorporate uncertainty into Pareto front optimization by simulating additional response surface model parameters according to a Multivariate Normal Distribution (MVN). This research shows that analogous to the univariate case, the MVN understates uncertainty, leading to overconfident conclusions when variance is not known and there are few observations (less than 25-30 per response). This research builds upon current methods using simulated response surface model parameters that are distributed according to an Multivariate t-Distribution (MVT), which can be shown to produce a more accurate inference when variance is not known. The MVT better addresses uncertainty in the parameters which can affect the frequency of treatments appearing on the Pareto front resulting in potentially different proposed solution spaces from that of the MVN.
AFIT Designator
AFIT-ENS-MS-20-M-136
DTIC Accession Number
AD1102509
Recommended Citation
Calhoun, Peter A., "Characterizing Uncertainty in Correlated Response Variables for Pareto Front Optimization" (2020). Theses and Dissertations. 3601.
https://scholar.afit.edu/etd/3601