2015 Informs Annual Meeting

SC75

INFORMS Philadelphia – 2015

SC72 72-Room 203A, CC

SC74 74-Room 204A, CC IEEE Intelligent Systems Invited Panel Discussion on Healthcare Intelligence Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Hui Yang, Associate Professor, Pennsylvania State University, 310 Leonhard Building, Industrial and Manufacturing Eng., State College, Moderator: Hui Yang, Associate Professor, Pennsylvania State University, 310 Leonhard Building, Industrial and Manufacturing Eng., State College, PA, 16801, United States of America, huy25@psu.edu, Panelists: W. Art Chaovalitwongse, Kwok Leu Tsui, Jing Li, Oguzhan Alagoz This panel brings a panel of distinguished experts to share their perspectives and answer questions pertaining to data sciences, operation research and healthcare research. Panelists are: Dr. TSUI Kwok Leung, City University of Hong Kong; Dr. Oguzhan Alagoz,University of Wisconsin-Madison; Dr. W. Art Chaovalitwongse, University of Washington; Dr. Jing Li; Arizona State University. SC75 75-Room 204B, CC Advanced Manufacturing Systems and Planning Cluster: Advanced Manufacturing Invited Session Chair: Jun-Qiang Wang, Professor, Northwestern Polytechnical University, Box 554, No. 127 West Youyi Road, Department of Industrial Engineering, Xi’an, 710072, China, wangjq@nwpu.edu.cn 1 - Real-time Data Driven Visual Decision Support System for the Factory Floor Mohammad Rahdar, Iowa State University, 133 University Village, Unit F, Ames, IA, 50010, United States of America, rahdar@iastate.edu, Guiping Hu, Dave Sly, Lizhi Wang The manufacturing industries face significant challenges in operational planning due to the uncertainties in demand, lead-time, logistic, etc. This study aims to improve the efficiency of the production planning system and provide the visibility of real-time operations. The decision support system can access real-time data and use the models and analytical techniques to support the manufacturing decision making. 2 - Data-based Scheduling System for Semiconductor Wafer Fabrication Facility Li Li, Professor, Tongji University, No.4800, Cao’an Road, Shanghai, China, lili@tongji.edu.cn Based on the analysis of the differences and relations between traditional and data-based scheduling methods, we propose a data-based scheduling framework and discuss how to implement it for a semiconductor manufacturing system. Then we introduce the state-of-the-art research on the key technologies of data- based scheduling and point out their development trends. Finally, we develop a data-based scheduling prototype system and also use some examples to demonstrate the superiority of the system. 3 - Cloud Manufacturing Ecosystem – Scheduling and Evolving Shengkai Chen, Zhejiang University, School of Mechanical In the Cloud Manufacturing Ecosystem, a benign mode and methodology for massive services schedule is required, in order for the collaboration within the whole Industry Chain. This paper studied the data of the resources and services, and modeled the services scheduling problem in the chain. With the Big Data analysis on the Cloud Platform, optimal assessment/schedule methods were developed, which could gradually evolve the ecosystem to an optimal situation. Engineering, 38# Zheda Road, Hangzhou, China, 372927638@qq.com, Shuiliang Fang, Haoke Peng PA, 16801, United States of America, huy25@psu.edu 1 - Panel Discussion on Healthcare Intelligence: Turning Data into Knowledge

Panel Discussion: IoT-enabled Data Analytics: Opportunities, Challenges and Applications Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Kaibo Liu, Assitant Professor, UW-Madison, 1513 University Avenue, Madison, 53706, United States of America, kliu8@wisc.edu 1 - Panel Discussion: loT-enabled Data Analytics: Opportunities, Challenges and Applications Moderator: Kaibo Liu, Assitant Professor, UW-Madison, 1513 University Avenue, Madison, 53706, United States of America, kliu8@wisc.edu, Panelists: Benoit Montreuil, George Q. Huang, Soundar Kumara, Diego Klabjan The goal of this session is to push the frontier in IoT application and the enabled data analytics research. The session provides a forum where participants can describe current research, identify important problems and areas of application, explore emerging challenges, and formulate future research directions. SC73 73-Room 203B, CC Quality Engineering Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Trevor Craney, Shell, Houston, TX, United States of America, Trevor.A.Craney@shell.com 1 - Integrated Approach for Field Reliability Prediction Based on Accelerated Life Testing To predict field reliability using analytic modeling, several important reliability activities should be conducted, including FMEA, stress and usage condition analysis, PoF, ALT, and cumulative damage modeling if needed. This paper builds an integrated process and comprehensive modeling framework, especially with cumulative damage rules when the certain field stresses are random processes. An engineering product is provided as an application of proposed method. 2 - The Constant Shape Parameter Assumption in Weibull Regression Steve Rigdon, Professor, Saint Louis University, 3545 Lafayette Ave, Salus 481, Saint Louis, MO, 63104, United States of America, srigdon@slu.edu, Georgia Mueller The usual assumption in Weibull regression is that the scale parameter is a function of the predictor variables and the shape parameter is constant. We consider the problem of estimating parameters in the presence of a nonconstant shape parameter and the effect of assuming a constant shape parameter when it really isn’t constant. The misspecification of a constant shape parameter leads to loss of power for tests of the slope parameters and inaccurate prediction intervals. 3 - Model Specification and Confidence Intervals for Voice Communication Sara Wilson, NASA Lnagley Research Center, Mail Stop 131, Hampton, VA, 23681, United States of America, sara.r.wilson@nasa.gov, Robert Leonard, David Edwards, Kurt Swieringa, Jennifer Kibler The performance of a system often depends on the accuracy of information transferred via voice communications. This paper presents a case study from a human-in-the-loop experiment using a simulated flight environment that required a complex voice clearance issued by Air Traffic Control to a flight crew. The lognormal and loglogistic distributions are found to model the time required for voice communication, and extensive investigation of outliers was performed to identify procedural anomalies. 4 - Determining Test Sample Size for Reliability Demonstration Retesting after Product Design Change Andre Kleyner, Global Reliability Engineering Leader, Delphi Electronics & Safety, 2151 E. Lincoln Rd, M.S. CTC4E, Kokomo, IN, 46902, United States of America, andre.v.kleyner@delphi.com, David Elmore, Benzion Boukai Last minute design changes after completed product testing is a common occurrence. It is also common for a redesigned product to be retested to demonstrate compliance to the original reliability requirements. This paper discusses the application of Bayesian techniques to reduce the sample sizes required for retesting after design change. The proposed method helps to reduce the test sample size while demonstrating the required reliability and helping to reduce the cost of product development. Mingxiao Jiang, Medtronic, 7000 Central Ave NE, Fridley, United States of America, mingxiao.jiang@medtronic.com

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