Informs Annual Meeting 2017

MB60

INFORMS Houston – 2017

MB60

MB62

370A Publishing in INFORMS Transactions on Education Sponsored: INFORMEd Sponsored Session Chair: Jeroen Belien, KU Leuven, KU Leuven, Brussel, 1000, Belgium, jeroen.belien@kuleuven.be 1 - Informs Transactions on Education (ITE): A General Overview Jeroen Belien, KU. Leuven, Warmoesberg 26, Brussel, 1000, Belgium, jeroen.belien@kuleuven.be The panelists include ITE editors and authors who have published recently in ITE. The authors will discuss their experiences with submitting articles to ITE. The editors will provide suggestions to authors who wish to submit their work to ITE; in particular, articles about case studies and about educational games. 2 - Panelist Stefan Creemers, IESEG France, Lille, France, sc@cromso.com 3 - Panelist Jill Hardin Wilson, Northwestern University, Industrial Engineering & Mgmt Sciences, 2140 Sheridan Road, Evanston, IL, 60208, United States, jill.wilson@northwestern.edu 4 - Panelist Ashlea Bennet Milburn, University of Arkansas, AR, United States, ashlea@uark.edu Chair: Karen T Hicklin, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States, khicklin@email.unc.edu Co-Chair: Julie Simmons Ivy, North Carolina State University, Raleigh, NC, 27695-7906, United States, jsivy@ncsu.edu 1 - Hidden Figures Panel Discussion Karen T.Hicklin, University of North Carolina at Chapel Hill, B-24 Hanes Hall, Chapel Hill, NC, 27599-3260, United States, khicklin@email.unc.edu The movie Hidden Figures (2016), based on the book by Margot Lee Shetterly, is a depiction of the untold story of black women mathematicians at NASA whose integral roles played a major part in some of America’s historical space achievements. In this session, we celebrate the achievements of black women and their contributions to advancements in operations research. The panel features women at different points in their careers and will highlight their work as operations researchers with the goal of encouraging diversity. 2 - Panelist Lauren Berrings Davis, North Carolina A&T.State University, 1601 E.Market Street, 404 McNair Hall, Greensboro, NC, 27411, United States, lbdavis@ncat.edu 3 - Panelist Tasha Inniss, INFORMS, Catonsville, MD, United States, TInniss@informs.org 4 - Panelist Harriet Nembhard, Oregon State University, Corvallis, OR, United States, harriet.nembhard@oregonstate.edu MB61 370B Hidden Figures Panel Discussion Sponsored: Minority Issues Sponsored Session

370C Quality Monitoring and Analysis in Complex Manufacturing Processes Sponsored: Quality, Statistics and Reliability Sponsored Session Chair: Weihong Guo, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, United States, wg152@rutgers.edu 1 - Software Reliability Modeling with Considering Randomness of Environmental Factors Mengmeng Zhu, Rutgers University, 1920 Aspen Court, Piscataway, NJ, 08854, United States, mengmeng.zhu@rutgers.edu, Hoang Pham Software reliability gains more attention from the researchers and practitioners in the past few decades. Lots of environmental factors defined by many studies can affect software reliability during the development process. However, most existing software reliability models have not incorporated these environmental factors. We propose a theoretic software reliability model with considering the fault detection rate is a stochastic process due to the randomness of the environmental factors. Examples are included to demonstrate the effectiveness and predictive power of the proposed model. 2 - Ensemble Modeling of in Situ Variables for Printed Electronics Manufacturing with in Situ Process Control Potential Hongyue Sun, University at Buffalo, Buffalo, NY, United States, hongyues@buffalo.edu, Yifu Li, Ran Jin Aerosol jet® printing is a printed electronics process that is capable to print various materials with fine features. In this paper, an ensemble model strategy is proposed to quantify the effect of the process setting variables on the in situ variables, and the effect of the in situ variables on the product quality in a two- level hierarchical way. By identifying significant in situ variables as responses for the process setting variables, as well as significant predictors for the product quality in a joint model estimation problem, the proposed model has a hierarchical variable relationship to enable in situ process control. A case study is used to demonstrate the advantages of the proposed method. 3 - Optimize Wind Farm Development with Hybrid Wind Biorefinery Renewable Energy System in Micro Grid Community Qing Li, Rutgers University, Piscataway, NJ, United States, ql78@scarletmail.rutgers.edu, Honggang Wang As one of the most promising renewable energy sources, wind power has been widely applied to meet the ever growth of energy consumption. Energy production varies due to wind fluctuation, thus constantly meeting community’s demand while maintaining cost efficiency is not always the case. In this study, the food waste from local community serves as energy recovery source in biorefinery to produce baseline energy. Computational models and optimization methods are developed for optimal development of wind farm in hybrid wind-biorefinery energy system to meet community demand. Two-stage optimization framework is proposed to search and improve the optimal number and placement of turbines. 4 - Nonparametric Change-point Detection for Process Monitoring and Prognostics in Advanced Manufacturing Weihong Guo, Rutgers, The State University of New Jersey, 96 Frelinghuysen Rd, CoRE Rm 220, Piscataway, NJ, 08854, United States, wg152@rutgers.edu, Shenghan Guo It is essential to detect significant changes to the production processes. In this study, we develop a monitoring and prognostics method that implements non- parametric change-point detection methods on non-stationary time series data collected from a real automobile manufacturing system. For offline change-point detection, we integrate three non-parametric methods: LASSO, threshold TLASSO, and wild binary segmentation (WBS); for online change-point detection, we implement the modified sequential change-point detection and multivariate adaptive regression splines. These nonparametric methods are compared in case studies with real manufacturing data.

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