Preserving Diversity via Robust Optimization

该奖项将通过为保护生物多样性的保护计划的战略设计开发大规模定量模型来支持国家健康和繁荣。 生物多样性对人类在地球上的生存至关重要。 保护生物多样性的一个关键战略是，随着时间的推移，做出战略性的土地使用决策，以建立一个栖息地保护区系统。 这些保护区的长期规划面临着挑战，因为缺乏关于物种流行率和恢复力的数据，土地开发活动的不确定性，以及土地维持不同物种的能力随时间的变化。 该项目采用数据驱动、计算效率高的稳健优化方法，可以帮助土地利用规划者做出影响野生动物和渔业管理以及可能影响周边社区繁荣的决策。该项目将利用与美国地质调查局以及Panthera和野生动物保护协会正在进行的合作。 项目执行人将通过南加州大学的妇女参与科学和工程方案，让女研究生和女本科生参与该项目。此外，他们将把这项研究的成果整合到他们教授的研究生和本科生课程中。该项目采用严格的定量方法进行保护规划，利用土地征用和开发数据为新颖的多阶段稳健优化模型提供信息。 该框架为内生不确定性下的优化问题提供了一种通用的建模方案。 这些模型涉及二进制自适应决策变量与指数数量的突发事件，其发生的机会也是决策相关的。 利用从政府机构和非政府组织获得的土地利用预测构建时空不确定性集。 PI将K-适应性方法扩展到多阶段设置，并将调查技术，以提供最优差距的下限。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This award will support national health and prosperity by developing large-scale quantitative models for the strategic design of conservation plans to preserve biodiversity. Biodiversity is essential to human survival on Earth. A key strategy for preserving biodiversity involves making strategic land use decisions over time to create a system of habitat reserves. Long-term planning for these reserves faces challenges due to scarcity of data on species prevalence and resilience, uncertainty about land development activities, and changes over time in the ability of land tracts to sustain diverse species. This project uses a data-driven, computationally efficient robust optimization approach that can assist land use planners in making decisions that affect wildlife and fishery management and that may impact the prosperity of surrounding communities. The project will leverage ongoing collaborations with the United States Geological Survey, as well as Panthera and the Wildlife Conservation Society. The PIs will involve female graduate and undergraduate students in the project through the Women in Science and Engineering program at the University of Southern California. In addition, they will integrate the outcomes of this research into graduate and undergraduate courses they teach.The project take a rigorous, quantitative approach to conservation planning that utilizes land acquisition and development data to inform novel multi-stage robust optimization models. The framework provides a general modeling scheme for optimization problems under endogenous uncertainty. These models involve binary adaptive decision variables with an exponential number of contingencies whose chance of occurrence is also decision-dependent. Spatio-temporal uncertainty sets are constructed using land use projections obtained from governmental agencies and NGOs. The PIs will extend the K-adaptability approach to the multi-stage setting, and will investigate techniques to provide lower bounds on the optimality gap.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.