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

EARLY概念探索性研究（EAGER）项目将生成能源，食品和交通基础设施相互依赖性的数据，为改进解决相互依赖的关键基础设施（ICI）设计和运营问题的方法提供重要信息。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ICI的社会，机械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储存库免费提供。沿着学术出版物和介绍，该项目将制作传播材料，如数字地图，分发给从业人员，特别是农村社区内的从业人员。这项研究的发现和成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物/能量模块，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（随机双层优化），以生成关于工业消费指数的合成数据。该项目还将提供与这些数据相关的不确定性措施，允许衡量质量以及量化与输入信息的关系。这种方法将为相互依赖的农业、食品、能源和交通运输ICI生成数据，从而可以整合现有的数据生成技术。随机二层优化问题的数学结构允许表示和集成的物理，机械和社区功能的ICI。其目的是生成数据，以优化备灾、抗灾和应对战略。 这项研究将有助于粮食系统复原力的新兴领域，并将使未来的努力，以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.