Maximizing quantitative traits in the mating design problem via simulation-based Pareto estimation. Hunter, S. R. & McClosky, B. IIE Transactions, 48(6):565–578, 2016. Honorable Mention (Second Place), 2017 IIE Transactions Focused Issue on Operations Engineering & Analytics Best Applications Paper Award.
Maximizing quantitative traits in the mating design problem via simulation-based Pareto estimation [pdf]Paper  doi  abstract   bibtex   
Commercial plant breeders improve economically important traits by selectively mating individuals from a given breeding population. Potential pairings are evaluated before the growing season using Monte Carlo simulation, and a mating design is created to allocate a fixed breeding budget across the parent pairs to achieve desired population outcomes. We introduce a novel objective function for this mating design problem that accurately models the goals of a certain class of breeding experiments. The resulting mating design problem is a computationally burdensome simulation optimization problem on a combinatorially large set of feasible points. We propose a two-step solution to this problem: (i) simulate to estimate the performance of each parent pair, and (ii) solve an estimated version of the mating design problem, which is an integer program, using the simulation output. To reduce the computational burden when implementing steps (i) and (ii), we analytically identify a Pareto set of parent pairs that will receive the entire breeding budget at optimality. Since we wish to estimate the Pareto set in step (i) as input to step (ii), we derive an asymptotically optimal simulation budget allocation to estimate the Pareto set that, in our numerical experiments, out-performs the Multi-objective Optimal Computing Budget Allocation (MOCBA) in reducing misclassifications. Given the estimated Pareto set, we provide a branch and bound algorithm to solve the estimated mating design problem. Our approach dramatically reduces the computational effort required to solve the mating design problem when compared to naive methods.

Downloads: 0