Transient Analysis of Manufacturing Systems: A Simulation-Based Transfer Function Modeling Approach for Responsive Production Planning

该奖项支持基础研究和统计方法的开发，用于实时预测制造系统的输出性能。这项研究是必要的，因为现代制造企业面临的挑战，动态优化或重新优化其生产计划，以应对中断，如供应商故障，需求预测不匹配和自然灾害。他们面临的问题是“哪种计划将导致系统的最佳性能？他说：“系统表现对生产计划的依赖是非线性和时间性的，不能用现有的方法如电脑模拟和生产分析来充分量化。前者（模拟）耗时，后者（验证）福尔斯精度较低。为了克服这些缺点，所提出的工作集成了自适应统计方法，仿真和分析的预测模型，与准确预测系统输出的任何输入计划，及时的目标。在这项工作中，制造系统的输入输出动态描述的一组传递函数模型（TFM）。TFMs是基于大量的仿真来估计的，因此它们体现了对真实的系统的仿真的高保真度; TFMs是数学差分方程，因此可以被实时评估用于“如果”分析。从这项工作中产生的方法和工具将帮助工业制造商在其管理活动中建立弹性，并提高其应对破坏性事件和从破坏性事件中恢复的能力。所提出的方法的应用不仅限于制造业，而且还可以扩展到服务，通信和运输，以提高管理质量。课堂案例研究将从这项研究中得出，并在本科和研究生阶段教授。该项目还包括让学生，特别是代表性不足的学生参与积极研究的举措。

This award supports basic research and development of a statistical methodology for real-time prediction of output performance of manufacturing systems. The research is needed because modern manufacturing enterprises face the challenge of dynamically optimizing or re-optimizing their production plans in response to disruptions such as supplier failures, demand-forecast mismatches and natural disasters. The question they face is "which plan will lead to the best performance of the system?" The dependence of the system performance upon the production plan is nonlinear and time-dependent, and cannot be adequately quantified by the existing methods such as computer simulation and queueing analysis. The former (simulation) is time-consuming, and the latter (queueing) falls short in accuracy. To overcome these shortcomings, the proposed work integrates adaptive statistical methods, simulation and analytical queueing models, with the goal of accurately predicting the system output for any input plan in a timely manner. In this work, the input-output dynamics of a manufacturing system are described by a set of transfer function models (TFMs). The TFMs are estimated based on extensive simulation and thus they embody the high-fidelity of simulation to real systems; the TFMs are mathematical difference equations, and hence can be evaluated in real-time for "what if" analysis. The methods and tools resulting from this work will help industrial manufacturers to build resilience into their management activities, and improve their ability to respond to and recover from disruptive events. Application of the proposed methods is not restricted to manufacturing, but can also be extended to service, communication and transportation for improved quality of management. Classroom case studies will be derived from this research and taught at both undergraduate and graduate levels. This project also includes initiatives to involve students, especially underrepresented students, in active research.