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• W2117576669 abstract "Papers in this special issue were selected from presentations made at the INFORMS 2009 Annual Meeting in San Diego, California. The INFORMS Quality Statistics and Reliability Section (QSR) organized an extensive four-day track comprising 48 sessions for that meeting, with more than 150 talks. Each fall, the INFORMS Annual Meeting offers an impressive selection of presentations in the QSR field. We are very grateful for the contributions of the authors who submitted papers. After two rounds of rigorous reviews, 10 papers were accepted for publication. Among them, five papers focus on general methodological development. The first paper, by J. Liu, provides a comparison survey on Statistical Process Control (SPC) and Stream-of-Variation (SOV) methodologies used for variation reduction of Multistage Manufacturing Processes (MMP). In each methodology, different approaches for variation modeling, monitoring, and root cause diagnosis are reviewed. This is followed by a summary of each methodology's contribution and limitations. The comparison is also illustrated through an example in which the two methodologies are applied to a machining process. The second paper, by H. Bush et al., proposes a new nonparametric multivariate control chart, called the kLINK chart, based on a k-linkage ranking algorithm that calculates the ranking of a new observation relative to the in-control training data. The kLINK chart provides a control boundary with a flexible shape to accommodate the non-parametric distributions of data points. Simulation studies show that the kLINK chart outperforms the existing Hotelling's T 2 and ranking depth control charts. The third paper, by Z. Li et al., proposes a systematic procedure to identify the difference between the Cox PH models fitted from different data sets. Cox PH models have been widely used in survival analysis for relating the occurrence of failure events to influential covariates. The proposed procedure aims to identify if there is a difference between the Cox PH models and whether to attribute the difference to the effects of the covariates or the baseline survival functions. The method is applied to comparing the Cox PH models corresponding to two usage periods of a CT scanner and finds different usage patterns reflected by the difference in the effects of the covariates. The fourth paper, by P. Jiang et al., studies the issue of estimating the reliability function of a Weibull distribution with zero failure data. A Shrinkage Preliminary Test Estimator (SPTE) is developed to combine the reliability function estimated from zero failure data of the target product and a prior guess on the reliability obtained from similar products. In selecting the shrinkage factor that determines the combination ratio, a measure of dissimilarity between the target product and the products from which the prior guess is obtained is defined based on key product quality characteristics. The fifth paper, by Y. Lei et al., proposes a method for reliability assessment of industrial networked automated systems. The method combines physical and data-link layer data of the network to restore complete communication log information, followed by an algorithm for predicting the network's time to failure. Five other papers address quality and reliability issues driven by specific applications. Diverse application areas are covered including transportation, offshore oil installation, and nano-composite, semiconductor, and assembly manufacturing processes. The sixth paper, by R. Ganesan et al., proposes an approximate dynamic programming approach for taxi-out time prediction. Taxi-out time of a flight is an important indicator of airport surface congestion. The proposed method enables prediction adapted to highly dynamic airport environments. The method is applied to the FAA database on several major airports and is demonstrated to outperform conventional predictive models such as linear regression, running average, and queuing models. The seventh paper, by A. R. Conn et al., proposes a simulation approach, based on the Semi-Markov Model, for maintenance scheduling of offshore oil installations with the objective of minimizing production loss. The proposed model is applied to the offshore installations operated by Statoil and the results are well received by the company's operational personnel. The eighth paper, by O. A. Vanli et al., proposes a Gaussian process model for combining the data from physical and computer experiments in order to predict the quality output of a nano-composite manufacturing process. Viewing from the Bayesian modeling perspective, the proposed model uses the computer data as the prior, which is then location and scale adjusted by the physical data to form the posteriors for the prediction. The proposed model achieves better accuracy than models based on computer or physical data alone. The ninth paper, by H. Kim et al., presents a case study on optimizing the photolithography stage of a semiconductor fabrication process. A multivariate normal linear model is used to link process input variables with critical dimension (CD) measures of wafers. Based on the model, optimal input setting is further identified to optimize the uniformity and target achievement of CD. The tenth paper, by T. Chaipradabkiat and J. Jin, proposes an approach to determine tooling adjustment strategies for achieving acceptable product quality in assembly processes. A linear model is developed to link tooling locating positions with product quality. Based on the model, mean shifts of the tooling location positions are estimated from product measurements. A tooling strategy is then proposed to ensure acceptable product fraction nonconforming. Finally, we would like to express our sincere thanks to the reviewers for their valuable and timely evaluation of the manuscripts, and to the Chief Editors, Douglas Montgomery and Aarnout Brombacher, for providing this opportunity to share with QREI readers the outstanding research efforts of INFORMS members." @default.
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• W2117576669 date "2010-10-28" @default.
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• W2117576669 title "Editorial-INFORMS 2009 annual meeting" @default.
• W2117576669 doi "https://doi.org/10.1002/qre.1164" @default.
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