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

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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.