Degree Type

Thesis

Date of Award

2016

Degree Name

Master of Science

Department

Industrial and Manufacturing Systems Engineering

Major

Industrial and Manufacturing Systems Engineering

First Advisor

Lizhi Wang

Abstract

In order to feed the world’s growing population, an interdisciplinary effort is needed. In this thesis, operations research tools of mathematical modeling, optimization, and simulation are used to improve an existing plant breeding method, genomic selection. To do this, a new method, called optimal population value (OPV) selection, is proposed. In this paper, OPV selection is first defined as an optimization problem that selects a breeding population using a population metric, instead of individual metrics. Then, OPV selection is thoroughly tested in a simulation study against the existing methods of genomic selection, weighted genomic selection, and optimal haploid value selection. From the results of the simulation study, up to an 8.3%, or 0.58 base standard deviations, greater mean response can be expected than when using traditional genomic selection. These results suggest that population-based selection methods are a promising future research direction.

DOI

https://doi.org/10.31274/etd-180810-5332

Copyright Owner

Matthew Daniel Goiffon

Language

en

File Format

application/pdf

File Size

66 pages

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