Cameron John N, Han Ye, Wang Lizhi, Beavis William D
Department of Agronomy, Iowa State University, Ames, IA, 50010, USA.
Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, 50010, USA.
Theor Appl Genet. 2017 Oct;130(10):1993-2004. doi: 10.1007/s00122-017-2938-9. Epub 2017 Jun 24.
Using an Operations Research approach, we demonstrate design of optimal trait introgression projects with respect to competing objectives. We demonstrate an innovative approach for designing Trait Introgression (TI) projects based on optimization principles from Operations Research. If the designs of TI projects are based on clear and measurable objectives, they can be translated into mathematical models with decision variables and constraints that can be translated into Pareto optimality plots associated with any arbitrary selection strategy. The Pareto plots can be used to make rational decisions concerning the trade-offs between maximizing the probability of success while minimizing costs and time. The systematic rigor associated with a cost, time and probability of success (CTP) framework is well suited to designing TI projects that require dynamic decision making. The CTP framework also revealed that previously identified 'best' strategies can be improved to be at least twice as effective without increasing time or expenses.
我们采用运筹学方法，展示了针对相互竞争的目标设计最优性状渗入项目的过程。我们展示了一种基于运筹学优化原理设计性状渗入（TI）项目的创新方法。如果TI项目的设计基于清晰且可衡量的目标，那么它们可以转化为具有决策变量和约束条件的数学模型，这些模型可以转化为与任何任意选择策略相关的帕累托最优图。帕累托图可用于在最大化成功概率的同时最小化成本和时间之间进行权衡时做出合理决策。与成本、时间和成功概率（CTP）框架相关的系统严谨性非常适合设计需要动态决策的TI项目。CTP框架还表明，先前确定的“最佳”策略可以在不增加时间或费用的情况下提高到至少两倍的效果。
