Adaptive Robust Optimization with Endogenous Uncertainty and Active Learning in Smart Manufacturing

智能制造中具有内生不确定性和主动学习的自适应鲁棒优化

• 批准号: 2030296
• 负责人: Qi Zhang
• 金额: $ 30.67万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030296&HistoricalAwards=false
• 关键词: Adaptive; Robust; Optimization; Endogenous; Uncertainty

This award will contribute to the national prosperity by providing rigorous methods to manage uncertainty in complex systems, with a particular focus on manufacturing systems. Uncertainty is pervasive in manufacturing, and safe and efficient operation under uncertainty, as well as effective risk management, is critically important to the nation's manufacturing competitiveness. This project develops methods to support operational control that explicitly consider endogenous uncertainty, that is, uncertainty that depends directly on decisions. In the manufacturing environment, significant uncertainty is due to the health states of physical assets, and managing these assets effectively constitutes a major challenge. The resulting methods, while validated on manufacturing systems, are expected to be widely applicable to other complex systems, such as energy systems. This project integrates an adaptive, set-based robust optimization approach incorporating endogenous uncertainty with active learning methods that collect and analyze data on an ongoing basis. Robust optimization presents a tractable approach to advance the state of the art in operational control by addressing various types of endogenous uncertainty. The project is expected to deepen our understanding of the dependence of uncertainty on decisions, provide a set-based robust optimization perspective on endogenous uncertainty, and introduce a new approach to modeling active learning and optimizing the trade-off between exploration and exploitation. The theoretical advances will be motivated by challenges in smart manufacturing, namely in integrated production and maintenance planning in networked production environments, The PI will include graduate and undergraduate students in the research, and will incorporate optimization methods in capstone design courses in chemical engineering.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.

该奖项将通过提供严格的方法来管理复杂系统中的不确定性，并特别关注制造系统，从而为国家繁荣做出贡献。不确定性在制造业中无处不在，在不确定性下安全高效的运营以及有效的风险管理对国家制造业的竞争力至关重要。该项目开发了支持运营控制的方法，明确考虑内生不确定性，即直接依赖于决策的不确定性。在制造业环境中，由于实物资产的健康状况，存在重大不确定性，有效管理这些资产构成重大挑战。所得到的方法虽然在制造系统上得到了验证，但预计将广泛适用于其他复杂系统，如能源系统。该项目将一种自适应的、基于集合的稳健优化方法与主动学习方法相结合，该方法将内生不确定性与主动学习方法结合在一起，这些方法不断收集和分析数据。稳健优化提供了一种易于处理的方法，通过解决各种类型的内生不确定性来提高操作控制的最新水平。该项目有望加深我们对不确定性对决策的依赖性的理解，提供一种基于集合的内生不确定性的稳健优化观点，并引入一种新的方法来建模主动学习和优化勘探和开发之间的权衡。理论的进步将受到智能制造中的挑战的推动，即在网络生产环境中的集成生产和维护计划，PI将包括研究生和本科生参与研究，并将优化方法纳入化学工程的顶峰设计课程。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Feasibility/Flexibility-based optimization for process design and operations

• DOI: 10.1016/j.compchemeng.2023.108461
• 发表时间: 2023-10
• 期刊: Comput. Chem. Eng.
• 作者: Huayu Tian;Jnana Sai Jagana;Qi Zhang;M. Ierapetritou

Capacity planning with uncertain endogenous technology learning

不确定内生技术学习的能力规划

• DOI: 10.1016/j.compchemeng.2022.107868
• 发表时间: 2022
• 期刊: Computers & Chemical Engineering
• 影响因子: 4.3
• 作者: Rathi, Tushar;Zhang, Qi
• 通讯作者: Zhang, Qi

Capacity planning for sustainable process systems with uncertain endogenous technology learning

具有不确定内生技术学习的可持续过程系统的能力规划

• 发表时间: 2022
• 期刊: Computer aided chemical engineering
• 作者: Rathi, Tushar;Zhang, Qi
• 通讯作者: Zhang, Qi