CAREER: Dynamic Invasive Species Control Optimization Via Integrated Education and Research (DISCOVER)

1554018(Buyuktahtakin)。这项研究的目标是设计最优的干预和资源分配策略，以保护生态系统免受入侵物种的有害影响。这一研究目标将通过四个具体目标来实现：(1)设计一个新的数学优化框架；(2)研究一类新的博弈论问题，以协调入侵物种管理中的多个利益相关者(如政府、土地管理人员、合作社)，并为公共政策决策提供洞察力；(3)制定和解决不确定条件下的新监视和控制问题；以及(4)通过实际案例进行实验，以展示所提出的模型和算法的潜在好处。这一职业计划的教育目标是创建决策模型，用于外展、教育和管理活动；作为回报，与利益相关者、学生和教师的互动将为设计尖端和实用的数学模型提供反馈。研究努力将包括：(1)设计一种新的基于分解的优化框架，将有助于克服时空优化中计算复杂性的巨大挑战；(2)开发严格的优化模型和方法，以解决环境管理中的高维资源分配问题；(3)开发决策支持工具，与生态学家、管理者和政策制定者合作，为入侵物种管理提供有效的管理实践；(4)通过对不确定性的严格分析和新颖的监视和控制建模，在理论和方法方面取得进展；(5)一类新的博弈论模型和算法，可用于为环境管理的公共政策设计提供基本见解；以及(6)离散优化方面的建模、算法和计算方面的进展，这可能有助于解决其他重要的优化问题。该奖项由CBET/ENG环境可持续发展项目和BioMaps下属的DMS/MPS部门共同资助。

1554018 (Buyuktahtakin). The goal of this research is to design optimal intervention and resource allocation strategies to protect ecological systems from the detrimental impacts of invasive species. This research goal will be pursued through four specific objectives: (1) designing a novel mathematical optimization framework; (2) investigating a new class of game theoretic problems to coordinate multiple stakeholders (e.g., government, land managers, cooperatives) in invasive species management and provide insight into public policy decisions; (3) formulating and solving novel surveillance and control problems under uncertainty; and (4) performing experiments to demonstrate the potential benefit of the proposed models and algorithms by using real-life cases. The education objective of this CAREER program is to create decision models for use in outreach, educational, and management activities; in return, interactions with stakeholders, students, and teachers will provide feedback for designing cutting-edge and practical mathematical models.The research efforts will include: (1) design of a novel decomposition-based optimization framework that will help to overcome a big challenge of computational complexity in spatio-temporal optimization; (2) development of rigorous optimization models and methods to solve high-dimensional resource allocation problems in environmental management; (3) development of a decision support tool to provide efficient management practices for invasive species management, in collaboration with ecologists, managers, and policy makers; (4) advancements in theory and methodology through a rigorous analysis of uncertainty and novel surveillance and control modeling; (5) a new class of game theoretic models and algorithms that can be used to provide fundamental insights into the design of public policies for environmental management; and (6) modeling, algorithmic, and computational advances in discrete optimization, which potentially may help solve other important optimization problems.This award is co-funded by the CBET/ENG Environmental Sustainability program and the DMS/MPS Division under BioMaPs.