CAREER: Robust Modeling and Analysis of Discrete-Event Dynamic Systems

该补助金为开发新方法和支持理论提供资金，用于使用数学规划对大规模随机离散事件动态系统进行鲁棒建模和分析。研究内容包括离散事件系统的鲁棒建模和灵敏度分析，以及系统动力学的结构特性分析。该工作将联合收割机仿真和数学规划方法相结合，以获得更有效的仿真模型，预测仿真模型中的逻辑错误，并获得更鲁棒的仿真结果。离散事件系统动力学将被建模分析，以支持敏感性分析的模拟时，传统的方法失败。这将使建模人员能够从一次模拟运行中提取更多信息，有效利用非模拟实验的部分信息，并支持大规模系统的实时敏感性信息的应用。大规模的真实世界系统将为实施通过该项目开发的概念和方法提供一个试验平台。为本科生和研究生提供多学科研究机会将是本项目的重要成果，该项目的特点是综合教育计划，导致课程重组，导师活动和K-12推广。首先，新的数学编程为基础的方法，以实现模型的效率，减少误差，并在输入的不确定性和/或模型误差的存在下，离散事件模拟的鲁棒性实验将开发。其次，这项工作将导致更好地理解模拟系统动力学和分析方法。第三，实验人员将能够从一次模拟运行中提取更多信息，并应用强大的敏感性分析程序。最后，新的基于数学编程的离散事件仿真建模和分析的仿真方法将提高系统建模过程的有效性和效率，为广泛的实际应用。

This grant provides funding for the development of new methodologies and supporting theory for robust modeling and analysis of large-scale stochastic discrete-event dynamic systems using mathematical programming. The research will involve robust modeling and sensitivity analysis of discrete-event systems as well as structural property analyses of system dynamics. The work will combine simulation and mathematical programming approaches to get more efficient simulation models, predict logical errors in simulation models, and obtain more robust simulation results. Discrete-event system dynamics will be modeled analytically to support sensitivity analyses for simulation when traditional methods fail. This will enable modelers to extract more information from a single simulation run, make effective use of partial information for non-simulated experiments and support the application of real-time sensitivity information for large-scale systems. Large-scale, real-world systems will provide a test bed for implementation of the concepts and methodologies developed through the project. Multidisciplinary research opportunities for undergraduate and graduate students will be an important outcome of this project which features an integrated educational plan leading to course restructuring, mentorship activities, and K-12 outreach.Significant benefits are anticipated from this research. First, new math-programming-based approaches to achieving model efficiency, error reduction, and robust experiments for discrete-event simulation in the presence of input uncertainties and/or model errors will be developed. Second, the work will lead to an improved understanding of simulation system dynamics and analytical methodologies for analyzing them. Third, experimenters will be able to extract more information from a single simulation run and apply robust sensitivity analysis procedures. Ultimately, new math-programming-based simulation methodologies for modeling and analysis of discrete-event simulation will improve the effectiveness and efficiency of the systems modeling process for a wide range of practical applications.