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CAREER: OPTIMIZATION FORMULATIONS AND ALGORITHMS FOR THE ANALYSIS AND DESIGN OF HIERARCHICAL MODULAR SYSTEMS

职业：分层模块化系统分析和设计的优化公式和算法

基本信息

批准号：
1748516
负责人：
Victor Zavala Tejeda
金额：
$ 50.24万
依托单位：
University of Wisconsin-Madison
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1748516&HistoricalAwards=false
关键词：
CAREER OPTIMIZATION FORMULATIONS ALGORITHMS ANALYSIS

项目摘要

Modularization is a pervasive organization strategy that is used by living, socio-economic, and industrial systems to cope with complexity. Modular industrial systems are built from small-scale, standardized equipment modules, which perform well-defined tasks. Standardization and size reduction enables mass fabrication and fast deployment of equipment, which accelerates experimentation and learning and ultimately leads to technology cost reductions. Modular systems enable staged (sequential) investment strategies, which provide flexibility to mitigate market and regulatory uncertainties. They also facilitate exploitation of highly dispersed resources that are deemed too expensive to centralize. The goal of this CAREER project is to develop optimization formulations and algorithms that facilitate the analysis and design of hierarchical modular systems. These capabilities will be used to design flexible combined power fertilizer systems in rural areas that produce power, ammonia, and urea from distributed resources such as wind energy, natural gas, biomass, and organic waste. Current industrial-scale process systems are highly customized and involve logistically-complex, expensive and lengthy construction phases. Identifying technologies that are suitable for modularization and determining appropriate degrees of enterprise-wide modularity can improve operational flexibility and mitigate financial risk. Large-scale industrial process systems that benefit from the economies of scale can evolve into a hybrid state in which certain functions will be performed in small modular systems that increase flexibility. To model these systems the use of hierarchical graph abstractions is proposed to provide a natural framework for analysis and optimization of the benefits of modularity. Graph abstractions enable the use of techniques to properly organize process equipment units into tightly integrated modules and can be applied recursively at higher levels where modules represent subsystems, entire production facilities, and local/regional/global supply chain hubs. Hierarchical graph structures will be exploited by combinatorial optimization and multi-stage stochastic programming techniques to derive scalable design and investment strategies that mitigate markets and regulatory risk. The educational part of this project aims to incorporate new hierarchical decision-making concepts into the engineering curriculum, make the curriculum itself more modular and develop software tools that enable the design of complex hierarchical systems using crowd-sourcing. Planned outreach activities will provide K-12 students from schools with high enrollment of underrepresented minorities with opportunities to learn about the benefits of modular decision-making and motivate them to pursue career paths in STEM fields.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.

模块化是一种普遍存在的组织策略，被生活、社会经济和工业系统用来应对复杂性。模块化工业系统由小规模的标准化设备模块构建而成，这些设备模块执行定义良好的任务。标准化和尺寸减小使得设备的大规模制造和快速部署成为可能，从而加速了实验和学习，最终导致技术成本的降低。模块化系统支持分阶段（顺序）投资策略，提供灵活性以减轻市场和监管的不确定性。它们还有助于开发高度分散的资源，这些资源被认为过于昂贵而无法集中。CAREER项目的目标是开发优化公式和算法，以促进分层模块化系统的分析和设计。这些能力将用于在农村地区设计灵活的联合动力肥料系统，利用风能、天然气、生物质能和有机废物等分布式资源生产电力、氨和尿素。目前工业规模的工艺系统是高度定制的，涉及物流复杂、昂贵和漫长的建设阶段。识别适合模块化的技术并确定企业范围内的适当模块化程度可以提高操作灵活性并降低财务风险。受益于规模经济的大型工业过程系统可以演变成一种混合状态，其中某些功能将在小型模块化系统中执行，从而增加灵活性。为了对这些系统进行建模，提出了使用分层图抽象来为分析和优化模块化的好处提供一个自然的框架。图抽象支持使用技术将过程设备单元适当地组织成紧密集成的模块，并且可以递归地应用于更高级别，其中模块表示子系统、整个生产设施和本地/区域/全球供应链中心。分层图结构将通过组合优化和多阶段随机规划技术来开发可扩展的设计和投资策略，从而降低市场和监管风险。该项目的教育部分旨在将新的分层决策概念纳入工程课程，使课程本身更加模块化，并开发软件工具，使使用众包设计复杂的分层系统成为可能。计划中的外展活动将为来自少数族裔入学率较高的学校的K-12学生提供机会，让他们了解模块化决策的好处，并激励他们在STEM领域追求职业道路。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。