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.