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

制造系统的瞬态分析：用于响应性生产计划的基于仿真的传递函数建模方法

• 批准号: 1068131
• 负责人: Feng Yang
• 金额: $ 19.68万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-15 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1068131&HistoricalAwards=false
• 关键词: Transient; Analysis; Manufacturing; Systems; Simulation

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.

该奖项支持实时预测制造系统输出性能的统计方法的基础研究和开发。这项研究是必要的，因为现代制造企业面临着动态优化或重新优化其生产计划以应对供应商故障、需求预测不匹配和自然灾害等干扰的挑战。他们面临的问题是“哪个计划将带来系统的最佳性能？”系统性能对生产计划的依赖性是非线性和时间依赖性的，并且不能通过计算机模拟和排队分析等现有方法充分量化。前者（模拟）耗时，后者（排队）精度较差。为了克服这些缺点，所提出的工作集成了自适应统计方法、模拟和分析排队模型，其目标是及时准确地预测任何输入计划的系统输出。在这项工作中，制造系统的输入输出动态由一组传递函数模型（TFM）描述。 TFM是在广泛的仿真基础上估计出来的，因此体现了仿真对真实系统的高保真度； TFM 是数学差分方程，因此可以实时评估以进行“假设”分析。这项工作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，并提高其响应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，还可以扩展到服务业、通信和交通运输，以提高管理质量。课堂案例研究将从这项研究中衍生出来，并在本科生和研究生层次上教授。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。