2015 Informs Annual Meeting

TC74

INFORMS Philadelphia – 2015

4 - Optimization Model for Floating Carsharing System Planning Antonio Antunes, Professor, University of Coimbra, Dept. of Civil Engineering, Coimbra, Portugal, antunes@dec.uc.pt The focus of this presentation is an optimization model aimed to assist a floating carsharing company in the making of its key planning decisions – the area to be operated by the company (called home area), the price rate or rates to be charged to customers, the relocation operations to perform across zones of the home area, and, indirectly, the size of the fleet to be used by the company. The results that can be obtained through the model are exemplified for a real-world setting. TC72 72-Room 203A, CC DDDAS for Industrial and System Engineering Applications III Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Shiyu Zhou, Professor, University of Wisconsin-Madison, Department of Industrial and Systems Eng, 1513 University Avenue, Madison, WI, 53706, United States of America, shiyuzhou@wisc.edu Co-Chair: Yu Ding, Professor, Texas A&M University, ETB 4016, MS 3131, College Station, TX, United States of America, yuding@iemail.tamu.edu 1 - The Predict Project: Enhancing DDDAS/Infosymbiotics with Privacy and Security Vaidy Sunderam, Emory University, 400 Dowman Dr #W-401, Math & CS, Atlanta, GA, 30322, United States of America, vss@emory.edu, Li Xiong The ubiquitousness of mobile devices will greatly expand the applicability of DDDAS, provided privacy and security issues are addressed. The PREDICT project is developing: (1) approaches to assign data-targets to participants with privacy protection; (2) methods for aggregating and fusing data that quantify veracity of the data sources and maintain high fidelity; and (3) secure distributed computation for field- and region-level deployment of the DDDAS paradigm with adaptation and feedback. 2 - Securing Industrial Control Systems with Software-defined Networking Dong Jin, Assistant Professor, Illinois Institute of Technology, 10 W 31st Street, Stuart Building 226E, Chicago, IL, 60614, United States of America, dong.jin@iit.edu Modern industrial control systems (ICSes) are increasingly adopting Internet technology to boost control efficiency, which unfortunately opens up a new frontier for cyber-security. With the goal of safely incorporating existing networking technologies in ICSes, we design a novel software-defined networking (SDN) architecture for ICSes, with innovative security applications (e.g., network verification and intrusion detection) and rigorous evaluation using IIT’s campus microgrid. 3 - A DDDAS Approach to Distributed Control in Computationally Constrained Environments (UAV Swarms) Vijay Gupta, Univ. of Notre Dame, 275 Fitzpatrick Hl Engrng, Notre Dame, IN, 46556, United States of America, vgupta2@nd.edu, Greg Madey, Daniel Quevedo, Wann-jiun Ma In modern applications of distributed control, the traditional assumption of ample processing power at every time step at each agent can be challenged by use of processor intensive sensors such as cameras. Inspired by the Dynamic Data Driven Application System approach, we present an algorithm that shifts computational loads among the agents to guarantee performance in spite of reduced average processor availability. Analytical results and numerical simulations illustrate the approach. TC73 73-Room 203B, CC Quality Monitoring and Analysis in Complex Manufacturing Processes Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Li Zeng, Assistant Professor, Texas A&M University, Industrial and Systems Engineering, College Station, TX, 77843, United States of America, lizeng@tamu.edu Co-Chair: Qiang Zhou, Assistant Professor, City Univ of Hong Kong, Kowloon, Hong Kong, China, q.zhou@cityu.edu.hk

1 - Monitoring Uniformity of Particle Distributions in Manufacturing Processes using the K Function Xiaohu Huang, Graduate Student, City University of Hong Kong, 106B, Hall 8, Student Residence, CityU, Hong Kong, China, xhhuang6-c@my.cityu.edu.hk, Qiang Zhou Data in the form of spatial point patterns are frequently encountered in manufacturing processes. The distributional characteristics of a spatial point pattern can be summarized by functional profiles like K function. In this study, a Gaussian process is designed to characterize its behaviour under complete spatial randomness. A T2 control chart is proposed to monitor the uniformity of point patterns. 2 - Bayesian Hierarchical Linear Modeling of Profile Data with Apps to Quallity Control of Nanomanufacturing Jianguo Wu, Assistant Professor, University of Texas-El Paso, El Paso, TX, United States of America, jwu2@utep.edu, Yuhang Liu, Shiyu Zhou To achieve a highly automatic quality control, simultaneous profile monitoring and diagnosis is often required. This paper presents a general framework by using a hierarchical linear model to connect profiles with both explanatory variables and intrinsic processing or product parameters for simultaneous monitoring and diagnosis. The effectiveness is illustrated through numerical studies and applications to NDE profiles for quality control of nanocomposites manufacturing. 3 - Modeling of Optical Profiles in Low-E Glass Manufacturing Qian Wu, Graduate Student, Texas A&M University, Industrial and Systems Engineering, College Station, TX, 77843, United States of America, hi.wuqian@gmail.com, Li Zeng Quality of low-E glass is measured by optical profiles. This study considers modeling of the optical profile data in Phase I analysis. 4 - Wafer Yield Prediction Based on Virtual Metrology-generated Parameters Wan Sik Nam, Seoung Bum Kim/Korea University, Korea University, 145 Anam-ro, Seongbuk-, Seoul, Korea, Republic of, wansiknam@korea.ac.kr Yield prediction is one of the most important issues in semiconductor manufacturing. Especially, for a fast-changing environment of the semiconductor industry, accurate and reliable prediction techniques are required. In this study, we propose a procedure to predict wafer yield using process parameters generated from the virtual metrology of the semiconductor fabrication, which is based on a variety of regression and classification algorithms. Chair: Peihua Qiu, Professor, University of Florida, 2004 Mowry Road, Gainesville, FL, 32610, United States of America, pqiu@phhp.ufl.edu 1 - When Importance Sampling Meets Stochastic Simulation Models Eunshin Byon, Assistant Professor, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI, 48109, United States of America, ebyon@umich.edu, Youngjun Choe, Nan Chen Importance sampling has been used to improve the efficiency of simulations where the simulation output is uniquely determined, given a fixed input. We extend the theory of importance sampling to estimate a system’s reliability with stochastic simulations where a simulator generates stochastic outputs. Given a budget constraint on total simulation replications, we derive the optimal importance sampling density that minimize the variance of an estimator. 2 - QQ Models: Joint Modeling for Quantitative and Qualitative Quality Responses in Manufacturing System Ran Jin, Virginia Tech., Grado Department of Industrial and, Systems Engineering, Blacksburg, VA, 24061, United States of America, jran5@vt.edu, Xinwei Deng A manufacturing system with both quantitative and qualitative (QQ) responses is widely encountered. The QQ responses are closely associated with each other, but current methodologies often model them separately. This paper presents a novel modeling approach, called “QQ models”, to jointly model the QQ responses through a constrained likelihood estimation. Both simulation studies and a case study are used to evaluate the performance of the proposed method. TC74 74-Room 204A, CC Innovative Methods for System Informatics Sponsor: Quality, Statistics and Reliability Sponsored Session

343