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

EAGER：探索离散事件动力学来建模和控制智能制造系统

• 批准号: 1829238
• 负责人: Giulia Pedrielli
• 金额: $ 21.18万
• 依托单位: Arizona State University
• 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: 2018-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1829238&HistoricalAwards=false
• 关键词: EAGER; Exploring; Discrete; Event; Dynamics

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.

这个早期概念的探索性研究资助（EAGER）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力奠定了基础。尽管有机会，建模方法和实时控制方法继续面临两个主要挑战，即缺乏预测模型的动态演变的制造系统，以及缺乏实时优化和控制算法，以产生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重大进步。该研究与教育计划相结合，以加强教育和推广活动，在代表性不足的群体，如妇女在运筹学和管理科学社区。EAGER奖支持基础研究的方法，以控制复杂的制造系统，利用机器状态的实时信息和合成数据生成的过程模拟器。 这项研究将提供新的方法，有针对性的按需仿真，结合新的控制方法，支持工厂级的决策的基础上瞬时机器状态。 具体而言，一种新的智能制造系统的仿真和控制体系结构将解决以下主要研究目标：（1）构建近似和高保真的仿真，可以连续接收来自真实的系统的信息，并产生条件语句;和（2）定义一类动态性能指标进行控制和优化。 该项目将导致聚合状态模型和条件模拟器的反馈闭环预测控制器，有效地利用动态变化的系统状态信息，以动态地适应控制策略。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Transient and Steady-State Analysis of Multistage Production Lines With Residence Time Limits

• DOI: 10.1109/tase.2020.2979179
• 发表时间: 2021-01
• 期刊: IEEE Transactions on Automation Science and Engineering
• 影响因子: 5.6
• 作者: Feifan Wang;Feng Ju

Metamodel-Based Quantile Estimation for Hedging Control of Manufacturing Systems

基于元模型的制造系统套期保值控制分位数估计

• 发表时间: 2019
• 期刊: Proceedings of the Winter Simulation Conference
• 作者: Pedrielli, G.;Barton, R. R.
• 通讯作者: Barton, R. R.

An Extended Two-Stage Sequential Optimization Approach: Properties and Performance

扩展的两阶段顺序优化方法：属性和性能

• 发表时间: 2020
• 期刊: European journal of operational research
• 影响因子: 6.4
• 作者: Pedrielli, G.;Wang, S.;Ng, S.H.
• 通讯作者: Ng, S.H.

A Framework for Multi-fidelity Modeling in Global Optimization Approaches

全局优化方法中的多保真度建模框架

• 发表时间: 2019
• 期刊: Lecture notes in computer science
• 作者: Zelda B. Zabinsky, Giulia Pedrielli

Stochastic Gaussian Process Model Averaging for High-Dimensional Inputs

• DOI: 10.1109/wsc48552.2020.9384114
• 发表时间: 2020-12
• 期刊: 2020 Winter Simulation Conference (WSC)
• 作者: Maxime Xuereb;S. Ng;Giulia Pedrielli