EAGER: SSDIM: Generating Synthetic Data on Interdependent Food, Energy, and Transportation Networks via Stochastic, Bi-level Optimization

这个探索性研究（EAGER）项目将生成能源、食品和交通基础设施相互依赖的数据，为解决相互依赖的关键基础设施（ici）的设计和运营问题的改进方法提供基本信息。该项目引入了一个新的数学框架，并将其应用于数据生成，允许明确表示基础设施的相互依赖性。它允许将当前的数据生成技术集成到集成接口的社会、机制和物理方面的统一表示中。这项研究产生的所有数据将通过在线存储库免费提供。除了学术出版物和报告外，该项目还将制作诸如数字地图等传播材料，以便向从业人员，特别是在农村社区传播。本研究的发现和结果可能有助于提高公众对基础设施威胁和复原力的认识。该项目还将为小学生开发一个食品/能源模块，并为研究生研究人员提供支持。该项目将开发一个新的数学框架（随机双水平优化）来生成有关ici的综合数据。该项目还将提供与这些数据相关的不确定性度量，允许质量度量以及与输入信息的量化关系。这种方法将为相互依赖的农业、粮食、能源和交通信息集成系统生成数据，从而整合现有的数据生成技术。随机双层优化问题的数学结构允许表示和集成集成接口的物理、机制和社区功能。其目的是生成数据，以优化备灾、抗灾和救灾战略。这项研究将有助于新兴的粮食系统恢复力领域，并将使未来的努力能够模拟粮食系统的潜在威胁，如能源供应中断和燃料价格飙升。

This EArly-concept Grant for Exploratory Research (EAGER) project will generate data at the interdependencies of the energy, food, and transportation infrastructures, providing essential information for contributing to improved approaches to addressing design and operational issues for interdependent critical infrastructures (ICIs). The project introduces a new mathematical framework and applies it to data generation, allowing explicit representation of infrastructure interdependencies. It allows integration of current data generation techniques into a unified representation of societal, mechanistic, and physical aspects of ICIs. All data generated from this research will be made freely available through online repositories. Along with academic publications and presentations, the project will create communication materials such as digital maps for dissemination to practitioners, particularly within rural communities. The findings and outcomes from this research may serve to raise public awareness about infrastructure threats and resilience. This project will also develop a food/energy module for elementary schoolchildren, as well as support for graduate student researchers.This project will develop a new mathematical framework (Stochastic Bilevel Optimization) to generate synthetic data on ICIs. The project will also provide uncertainty measures associated with these data, allowing a measure of quality as well as quantifying relationships with input information. This method will generate data for interdependent agriculture, food, energy, and transportation ICIs, allowing for integration of existing data generation techniques. The mathematical structure of the stochastic bilevel optimization problem allows for representation of, and integration across, the physical, mechanistic, and community functions of ICIs. The aim is to generate data to optimize strategies for disaster preparedness, resilience, and response. This research will contribute to the emerging area of food system resilience and will enable future efforts to model potential threats to food systems such as energy supply disruptions and fuel price spikes.