Number of Replications Required in Monte Carlo Simulation Studies: A Synthesis of Four Studies

Monte Carlo simulations are used extensively to study the performance of statistical tests and control charts. Researchers have used various numbers of replications, but rarely provide justification for their choice. Currently, no empirically-based recommendations regarding the required number of replications exist. Twenty-two studies were re-analyzed to determine empirically-based recommendations

A Time Truncated Moving Average Chart for the Weibull Distribution

A control chart of monitoring the number of failures is proposed with a moving average scheme, when the life of an item follows a Weibull distribution. A specified number of items are put on a time truncated life test and the number of failures is observed. The proposed control chart has been evaluated by the average run lengths (ARLs) under different parameter settings. The control constant and the test time multiplier are to be determined by considering the in-control ARL. It is observed that the proposed control chart is more efficient in detecting a shift in the process as compared with the existing time truncated control chart. ? 2013 IEEE.11Ysciescopu

Development of a Hierarchical, Model-Based Design Decision-Support Tool for Assessing Uncertainty of Cost Estimates

In order to identify ways to improve cost estimation, especially early in design, cost estimation needs to be viewed and represented as a process. An important activity within the cost estimation process is assessing the cost risk of a system. A decision-support tool that assesses cost risk should represent the impact of subsystem or system-level uncertainty and provide mechanisms to help select among competing designs. In order to address these problems, a generic cost estimation process was developed. It is based on an extensive review of the cost estimation literature. Also, a hierarchicial product structure, model-based approach and tool to estimate system-level cost risk was developed. This tool provides a link between cost models and cost elements for each component, mechanisms for determining the impact of risk on the cost of the design, and outputs used for selecting among alternative competing designs

Practical Design of Generalized Likelihood Ratio Control Charts for Autocorrelated Data

Control charts based on Generalized Likelihood Ratio (GLR) tests are attractive from both a theoretical and practical point of view. In particular, in the case of an autocorrelated process, the GLR test uses the information contained in the time-varying response after a change and, as shown by Apley and Shi, is able to outperfom traditional control charts applied to residuals. In addition, a GLR chart provides estimates of the magnitude and the time of occurrence of the change. In this paper, we present a practical approach to the implementation of GLR charts for monitoring an autoregressive and moving average process assuming that only a Phase I sample is available. The proposed approach, based on automatic time series identification, estimates the GLR control limits via stochastic approximation using bootstrap resampling. Thus, it is able to take into account the uncertainty about the underlying model. A Monte Carlo study shows that our methodology can be used to design in a semi-automatic fashion a GLR chart with a prescribed rate of false alarms when as few as 50 Phase I observations are available. A real example is used to illustrate the designing procedure

Application of Intelligent-Objects Simulation to Manufacturing Process Efficiency Analysis: A Case Study

A thesis presented to the faculty of the College of Science and Technology at Morehead State University in partial fulfillment of the requirements for the Degree of Master of Science by Justin Hamilton on May 12, 2012

A Comparison of Some Robust Bicariate Control Charts for Individual Observations

This paper proposed and considered some bivariate control charts to monitor individual observations from a statistical process control. Usual control charts which use mean and variance-covariance estimators are sensitive to outliers. We consider the following robust alternatives to the classical Hoteling’s T2: T2MedMAD, T2MCD, T2MVE A simulation study has been conducted to compare the performance of these control charts. Two real life data are analyzed to illustrate the application of these robust alternatives