The world population is increasing rapidly and is projected to hit 9.1 billion by 2050.
As the demand for food increases, agriculture production will continue to play a significant
role. As a method to maintain and increase agriculture production, plant breeding is critical.
To improve efficiency in the plant breeding process, an interdisciplinary effort is needed.
Operations research as a discipline focuses on decision making and efficient and effective
strategy design. In this thesis, operations research tools of simulation, optimization and
mathematical modeling are applied to plant breeding, specifically Genomic Selection (GS).
GS techniques allow breeders to select the best plants to make crosses by predicting, for
example, the heights of the plants using the genotypic data at an early stage of the plant
growth cycle, saving both time and cost that would otherwise be necessary to grow the
plants to maturity before their heights can be measured. A major limitation of existing GS
approaches is the trade-off between short-term genetic gains and long-term growth potential.
Some approaches focus on achieving short-term genetic gains at the cost of losing genetic
diversity for long-term gains, and others aim to maximize the long-term genetic gains but
are unable to achieve it by the breeding deadline. Our contribution is to define a new look
ahead method for assessing a selection decision, which evaluates the probability to achieve
both genetic diversity and breeding deadline. Moreover, we propose a heuristic algorithm
to find an optimal selection decision with respect to the new method. Our new selection
method outperforms the other selection methods in the literature.
","abstract_html":"<p>The world population is increasing rapidly and is projected to hit 9.1 billion by 2050.</p> <p>As the demand for food increases, agriculture production will continue to play a significant</p> <p>role. As a method to maintain and increase agriculture production, plant breeding is critical.</p> <p>To improve efficiency in the plant breeding process, an interdisciplinary effort is needed.</p> <p>Operations research as a discipline focuses on decision making and efficient and effective</p> <p>strategy design. In this thesis, operations research tools of simulation, optimization and</p> <p>mathematical modeling are applied to plant breeding, specifically Genomic Selection (GS).</p> <p>GS techniques allow breeders to select the best plants to make crosses by predicting, for</p> <p>example, the heights of the plants using the genotypic data at an early stage of the plant</p> <p>growth cycle, saving both time and cost that would otherwise be necessary to grow the</p> <p>plants to maturity before their heights can be measured. A major limitation of existing GS</p> <p>approaches is the trade-off between short-term genetic gains and long-term growth potential.</p> <p>Some approaches focus on achieving short-term genetic gains at the cost of losing genetic</p> <p>diversity for long-term gains, and others aim to maximize the long-term genetic gains but</p> <p>are unable to achieve it by the breeding deadline. Our contribution is to define a new look</p> <p>ahead method for assessing a selection decision, which evaluates the probability to achieve</p> <p>both genetic diversity and breeding deadline. Moreover, we propose a heuristic algorithm</p> <p>to find an optimal selection decision with respect to the new method. Our new selection</p> <p>method outperforms the other selection methods in the literature.</p>","abstract_has_math":false,"creators":["Moeinizade, Saba"],"institution":null,"degree_name":"Master of Science","degree_level":"thesis","degree_discipline":"Industrial and Manufacturing Systems Engineering","degree_department":"Department of Industrial and Manufacturing Systems Engineering","school":null,"contributors":[],"advisors":["Guiping Hu"],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-01-01","date_published":"2018-01-01","updated_at":"2026-07-24T02:39:41Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["archive/lib.dr.iastate.edu/etd/16735/"],"render_values":[{"text":"archive/lib.dr.iastate.edu/etd/16735/","href":null,"code":true}]}]},"links":{"outbound_url":"https://dr.lib.iastate.edu/handle/20.500.12876/30918","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Guiping Hu"]},{"key":"dc:contributor.department","label":"Department","values":["Department of Industrial and Manufacturing Systems Engineering"]},{"key":"dc:creator","label":"Author","values":["Moeinizade, Saba"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-01-15T09:15:34.000"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-06-30T03:13:14Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-06-30T03:13:14Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-01-01"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial and Manufacturing Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["archive/lib.dr.iastate.edu/etd/16735/"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://dr.lib.iastate.edu/handle/20.500.12876/30918"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The world population is increasing rapidly and is projected to hit 9.1 billion by 2050.
As the demand for food increases, agriculture production will continue to play a significant
role. As a method to maintain and increase agriculture production, plant breeding is critical.
To improve efficiency in the plant breeding process, an interdisciplinary effort is needed.
Operations research as a discipline focuses on decision making and efficient and effective
strategy design. In this thesis, operations research tools of simulation, optimization and
mathematical modeling are applied to plant breeding, specifically Genomic Selection (GS).
GS techniques allow breeders to select the best plants to make crosses by predicting, for
example, the heights of the plants using the genotypic data at an early stage of the plant
growth cycle, saving both time and cost that would otherwise be necessary to grow the
plants to maturity before their heights can be measured. A major limitation of existing GS
approaches is the trade-off between short-term genetic gains and long-term growth potential.
Some approaches focus on achieving short-term genetic gains at the cost of losing genetic
diversity for long-term gains, and others aim to maximize the long-term genetic gains but
are unable to achieve it by the breeding deadline. Our contribution is to define a new look
ahead method for assessing a selection decision, which evaluates the probability to achieve
both genetic diversity and breeding deadline. Moreover, we propose a heuristic algorithm
to find an optimal selection decision with respect to the new method. Our new selection
method outperforms the other selection methods in the literature.
"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A stochastic simulation approach for improving response in genomic selection"]}]}],"canonical_facts":{"dc:contributor.advisor":["Guiping Hu"],"dc:contributor.department":["Department of Industrial and Manufacturing Systems Engineering"],"dc:creator":["Moeinizade, Saba"],"dc:date":["2019-01-15T09:15:34.000"],"dc:date.accessioned":["2020-06-30T03:13:14Z"],"dc:date.available":["2020-06-30T03:13:14Z"],"dc:date.issued":["2018-01-01"],"dc:description.abstract":["The world population is increasing rapidly and is projected to hit 9.1 billion by 2050.
As the demand for food increases, agriculture production will continue to play a significant
role. As a method to maintain and increase agriculture production, plant breeding is critical.
To improve efficiency in the plant breeding process, an interdisciplinary effort is needed.
Operations research as a discipline focuses on decision making and efficient and effective
strategy design. In this thesis, operations research tools of simulation, optimization and
mathematical modeling are applied to plant breeding, specifically Genomic Selection (GS).
GS techniques allow breeders to select the best plants to make crosses by predicting, for
example, the heights of the plants using the genotypic data at an early stage of the plant
growth cycle, saving both time and cost that would otherwise be necessary to grow the
plants to maturity before their heights can be measured. A major limitation of existing GS
approaches is the trade-off between short-term genetic gains and long-term growth potential.
Some approaches focus on achieving short-term genetic gains at the cost of losing genetic
diversity for long-term gains, and others aim to maximize the long-term genetic gains but
are unable to achieve it by the breeding deadline. Our contribution is to define a new look
ahead method for assessing a selection decision, which evaluates the probability to achieve
both genetic diversity and breeding deadline. Moreover, we propose a heuristic algorithm
to find an optimal selection decision with respect to the new method. Our new selection
method outperforms the other selection methods in the literature.
"],"dc:format.mimetype":["application/pdf"],"dc:identifier":["archive/lib.dr.iastate.edu/etd/16735/"],"dc:identifier.uri":["https://dr.lib.iastate.edu/handle/20.500.12876/30918"],"dc:language.iso":["en"],"dc:title":["A stochastic simulation approach for improving response in genomic selection"],"dc:type":["thesis"],"thesis:degree_discipline":["Industrial and Manufacturing Systems Engineering"],"thesis:degree_level":["thesis"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T02:39:41Z"}