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

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，特别是制造系统，为国家繁荣做出贡献。 不确定性在制造业中普遍存在，在不确定性下安全有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。 本项目开发的方法，以支持运营控制，明确考虑内生的不确定性，即不确定性，直接取决于决策。在制造业环境中，由于物理资产的健康状况而产生了重大的不确定性，有效地管理这些资产构成了一项重大挑战。 由此产生的方法，而验证制造系统，预计将广泛适用于其他复杂的系统，如能源系统。 该项目集成了一种自适应的，基于集合的鲁棒优化方法，将内源性不确定性与主动学习方法相结合，以持续收集和分析数据。 鲁棒优化提出了一种易于处理的方法，通过解决各种类型的内源性不确定性来推进操作控制中的最新技术。 该项目有望加深我们对不确定性对决策依赖性的理解，提供基于集合的内生不确定性鲁棒优化视角，并引入一种新的方法来建模主动学习和优化探索与开发之间的权衡。 理论上的进步将受到智能制造的挑战的推动，即在网络化生产环境中的集成生产和维护规划，PI将包括研究生和本科生，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

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.