Special Issue on INFORMS 2015 Annual Meeting

E ach year, the Institute for Operations Research and the Management Sciences (INFORMS) Annual Meeting provides an excellent platform for researchers world-wide to present their state-of-the-art research in quality and reliability. The objective of this Special Issue is to introduce the recent advancements in quality and reliability methodological studies as well as applications in a form of full-length papers expanded from the presentations in the INFORMS 2015 Annual Meeting (November 1–4, Philadelphia, PA). We are very grateful for the contributions of the authors who submitted papers. After two to three rounds of rigorous reviews, 10 papers were accepted for publication. Among them, five papers focus on process monitoring and fault identification. The paper by Turkoz et al. proposes a new fault identification method when a high dimensional process is out-of-control. The proposed method combines the support vector data description-based test statistic with an adaptive step-down procedure to identify the faults. By using a nonparametric one-class classification method, the proposed approach does not rely on any distribution assumption. Compared with the existing distribution free methods, the proposed method has much more stable performance when the number of faults is more than one. The paper by Choe et al. focuses on the off-line change-point detection problem for time-series data. It adopts the Thresholded Least Absolute Shrinkage and Selection Operator (LASSO) techniques to control the false positives. The authors demonstrated the superior performance of the proposed method by comparing with several benchmark methods based on both simulations and a case study related to solar panel performance. The paper proposed by Zang, Wang, and Jin is handling with the issue of monitoring of processes based on unaligned profiles. While existing works focus on monitoring of well-aligned profiles, this paper develops new algorithms for monitoring unaligned profiles with varying sampling points. For this, they propose a robust dynamic time warping algorithm for profile alignment that are robust to noises and shift signals. And then, they propose a penalization-based charting algorithm that gives more effective performance in shift detection. In order to illustrate their proposed framework, they applied it for unaligned profile monitoring to the ingot growth process and monitoring heating power profiles. The paper by Abdella et al. proposes the double Exponentially Weighted Moving Average based procedure to evaluate the quality of a process based on polynomial quality profiles. The simulations studies have revealed the distinctive performance of their proposed double Exponentially Weighted Moving Average based control charts in quickly detecting changes in the second-order polynomial profiles. Their extensive simulation studies are based on two shift patterns in the polynomial quality profiles: changes in the coefficients of the regression parameters and changes in the process variability. Timely detection of whether a data stream reaches the steady state is critical in various fields. The paper by Hou, Wu, and Chen presents a new online steady state detection algorithm under the Bayesian framework based on a multiple change-point state space formulation and the sequential Monte Carlo methods. In order to reduce the variance of Monte Carlo estimation and enhance the computational efficiency, they propose a Rao-Blackwellization technique. Both artificially simulated signals and a real data example from the ultrasonic-cavitation based nanoparticle dispersion process are used to demonstrate the robustness of their proposed algorithms for various types of signals with different levels of noises. Five other papers address other important quality and reliability issues such as forecasting, maintenance, and robust design. The paper by Xiang and Coit proposes a multi-criteria optimization model to jointly minimize the burning and maintenance costs of a product with heterogeneous subpopulations. The preventive maintenance is allowed to be imperfect. Two papers in this special issue use hybrid models for forecasting. The paper by Xu et al. develops a hybrid Autoregressive integrated moving average-Linear regression (ARIMA-LR) method that combines the ARIMA model and the linear regression model in a sequential manner to capture both the seasonal trend and effects of predictors in time series data. Real-world case studies regarding two Chinese emergence departments are conducted to illustrate the forecasting performance of the developed method. The paper by Xin et al. proposes a hybrid model of singular spectrum analysis and support vector regression to predict failure time series. A stepwise grid search algorithm is used to find optimal tuning parameters in the hybrid model. The paper by Soh, Kim, and Yum develops a multivariate loss function approach to multi-characteristic robust design problems with an appropriately defined signal-to-noise (SN) ratio. Existing methods do not properly consider the variance–covariance structures among performance characteristics and/or do not preserve the original properties of the Taguchi SN ratio. The paper by Zhang, Yang, and Xin proposes a semi-parametric microstructure modeling method that can capture the variation across different microstructure samples. Existing methods only consider a single microstructure sample, while the unit-to-unit variability among different samples is ignored. Their proposed model can be used for both isotropic materials, and anisotropic materials in which the microstructure properties in the vertical direction and those in the horizontal direction are different. They illustrate their proposed methods by applying them for the quality control of real-life dual phase steels.