EAGER: Exploring Discrete Event Dynamics to Model and Control Intelligent Manufacturing Systems

这个早期概念探索性研究资助(EARGER)项目将通过研究智能制造系统控制的新模型和优化技术来提高国家的竞争力。计算机控制流程、高性能计算和物联网(IoT)的快速发展为显著提高制造业生产率奠定了基础。尽管有这样的机会，但建模方法和实时控制方法仍然面临着两大挑战，即缺乏对制造系统动态演化的预测模型，以及缺乏实时优化和控制算法来生成有效的在线生产控制。该项目将解决这些挑战，这将导致制造实践的显著进步。这项研究与一项教育计划相结合，旨在加强在女性参与运筹学和管理科学界等代表性较低的群体中的教育和推广活动。这一热切的奖项支持通过利用机器状态的实时信息和过程中模拟器产生的合成数据来控制复杂制造系统的方法的基础研究。这项研究将为有针对性的按需仿真提供新的方法，并与新的控制方法相结合，以支持基于瞬时机器状态的工厂级决策。具体地说，一种新颖的智能制造系统仿真与控制体系结构将解决以下主要研究目标：(1)构建能够连续接收来自真实系统的信息并生成条件语句的近似和高保真仿真；(2)定义一类需要控制和优化的动态性能规范。该项目将导致聚合状态模型和条件模拟器馈送闭环系统预测控制器，从而有效地利用动态变化的系统状态信息来动态调整控制策略。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。

This EArly-concept Grant for Exploratory Research (EAGER) project will improve the nation's competitiveness by investigating new models and optimization techniques for control of intelligent manufacturing systems. Rapid advances in computer-controlled processes, high-performance computing, and Internet-of-Things (IoT) lay the groundwork for significantly improving manufacturing productivity. Despite the opportunity, modeling methodologies and real-time control methods continue to face two major challenges, namely the lack of predictive models for the dynamic evolution of manufacturing systems, and the lack of real-time optimization and control algorithms to generate effective on-line production control. This project will address these challenges, which will lead to significant advancement in manufacturing practice. The research is integrated with an education plan to enhance education and outreach activities in underrepresented groups such as Women in Operations Research and Management Science community.This EAGER award supports fundamental research in methods to control complex manufacturing systems by leveraging both real-time information on machine state and synthetic data generated by in-process simulators. This research will provide new methods for targeted on-demand simulation, integrated with novel control methodology, to support factory level decision-making based on instantaneous machine status. Specifically, a novel simulation and control architecture for intelligent manufacturing systems will address the following main research objectives: (1) construct approximate and high-fidelity simulations that can continuously receive information from the real system and generate conditional statements; and (2) define a class of dynamic performance specifications to be controlled and optimized. This project will lead to aggregate-state models and conditional simulators feeding closed loop predictive controllers that effectively utilize the dynamically changing system state information to dynamically adapt the control policy.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.