Combining Statistical Process Control and Optimization via Simulation for Robust Sensor Network Design in the Presence of Sensor Measurement Error

对于传感器网络设计，通常使用优化方法来确定安装多少传感器以及在物理上安装它们。一个研究流是假设传感器测量与正态分布噪声之间的已知函数关系（通常是回归线）。这种假设不适用于具有复杂动态的系统，如交通运输或环境监测。即使当过程模拟被用于捕获复杂的动态时，也经常假设来自随机模拟的估计的性能测量是准确的并且没有假警报（即，无传感器测量误差）。在这些假设下建立的传感器网络可能会产生不可接受的高误报率，最终使决策者放弃传感器网络。本项目开发了在复杂系统的传感器网络检测到水质监测网络中的污染物溢出等异常行为时发出警报的统计监测和控制方法。然后，它开发了有效的基于模拟的优化算法，以确定传感器的数量和它们的位置时，随机模拟是用来估计多个性能指标。最后结合统计方法和仿真优化方法设计出最优的传感器网络，同时控制误报率。这项研究是跨制造，质量控制，模拟和环境工程的跨学科;研究团队的组成扩大了研究和教学中代表性不足的群体的参与。本研究的成果适用于许多应用领域，因此将有利于美国的经济和社会。本项目的目标是开发方法，将是有用的，在确定一个最佳的传感器网络快速，准确地在传感器测量误差的存在下，一个复杂的系统，其控制和失控的观察，通过随机过程模拟获得。本计画考虑具有一般边际与一般相关结构之潜在大规模制程，以扩大统计制程控制方法之应用领域，并发展其控制极限既不需要底层制程之模型化，也不需要试错校正之统计制程控制技术。当存在多个性能指标时，它还通过仿真开发了SPC和离散优化的组合框架。最后，它有利于知识转移从IE/OR到非传统IE/OR领域的应用所产生的组合算法的水质监测问题。

For sensor network design, an optimization method is often used to determine how many sensors to install and where to physically install them. One stream of research is to assume a known functional relationship (usually a regression line) among sensor measurements with normally distributed noise. This assumption does not hold for systems with complex dynamics such as traffic transportation or environmental monitoring. Even when a process simulation is used to capture complex dynamics, it is often assumed that estimated performance measures from stochastic simulation are accurate and there is no false alarm (i.e., no sensor measurement error). A sensor network found under these assumptions may produce unacceptably high false alarm rates, which eventually makes the decision maker abandon the sensor network. This project develops statistical monitoring and controlling methods for raising an alarm when a sensor network of a complicated system detects abnormal behaviors such as a contaminant spill in a water quality monitoring network. Then it develops efficient simulation-based optimization algorithms to determine the number of sensors and their locations when stochastic simulation is used to estimate multiple performance measures. Finally this project combines the statistical methods and the simulation-based optimization to design the optimal sensor network while controlling false alarm rates. This research is interdisciplinary across manufacturing, quality control, simulation and environmental engineering; and the composition of the research team broadens the participation of underrepresented groups in research and teaching. The results from this research are applicable to many application areas and thus will benefit the U.S. economy and society. The objective of this project is to develop methods that will be useful in identifying an optimal sensor network quickly and accurately in the presence of sensor measurement error for a complicated system whose in-control and out-of-control observations are obtained through stochastic process simulation. This project considers a potentially large-scale process with general marginal and general correlation structure, which can broaden application fields of statistical process control (SPC) methods; and develops SPC techniques whose control limits neither require modeling of an underlying process nor trial-and-error calibration. It also develops a combined framework of SPC and discrete optimization via simulation when multiple performance measures exist. Finally it facilitates knowledge transfer from IE/OR to non-traditional IE/OR fields by applying the resulting combined algorithms to the water quality monitoring problem.