2015 Informs Annual Meeting

MD74

INFORMS Philadelphia – 2015

4 - Generalized Bounded Rationality and Robust Multi-Commodity Network Design Changhyun Kwon, Associate Professor, University of South Florida, 4202 East Fowler Avenue, ENB 118, Tampa, FL, 33620, United States of America, chkwon@usf.edu, Longsheng Sun, Mark Karwan When the route-choice behavior of network users are uncertain, the notion of bounded rationality has been used to allow users to choose sub-optimal routes whose length is within a certain bound. In this paper, we provide another framework to explain such bounded rationality assuming that network users make perfectly rational route decisions, but with perception error in link costs. By showing that some cases of the perception error model are equivalent to the bounded rationality models, we establish the notion of generalized bounded rationality. We demonstrate how the notion of generalized bounded rationality can be used for robust multi-commodity network design problems and provide computable optimization frameworks based on both links and paths. We illustrate our approaches in the context of hazardous materials transportation.

3 - An OSA Detection Approach using a Discriminative Hidden Markov Model Xi Zhang, Assistant Professor, Peking University, 5 Yiheyuan Rd., Beijing, 100871, China, xi.zhang@pku.edu.cn, Changyue Song, Kaibo Liu We proposed a novel detection approach for obstructive sleep apnea (OSA) based on ECG signals by considering the temporal dependency. A discriminative hiddern Markov model (HMM) and corresponding parameter estimation algorithms are provided, and a real case study shows that a competitive performance including accuracies of 94.3% for per-recording classification and 86.2% for per-segment OSA detection with satisfactory sensitivity and specificity were achieved. 4 - Quantification and Monitoring on Ecommerce Reviews Dataset Suoyuan Song, HKUST, Dept. of IELM, HKUST, Clear Water Bay, Kowloon, Hong Kong - PRC, songsuoyuan@gmail.com, Fugee Tsung Recently, the boom of e-merchants have attracted researchers on analyzing those text-rich data. Unfortunately, these technologies have drawn little attention in statistics and quality area. In this article, we aim to (1) use text mining technologies to first quantify customer reviews, and (2) build statistical model to monitor those text-rich reviews data. Chair: Yisha Xiang, Assistant Professor, Lamar University, 2626 Cherry Engineering Building, Beaumont,, TX, 77710, United States of America, yxiang@lamar.edu Co-Chair: David Coit, Professor, Rutgers University, Piscataway, NJ, United States of America, coit@rci.rutgers.edu 1 - A Gaming Model for Outsourcing Maintenance under Uncertain Fleet Expansion Tongdan Jin, Texas State University, 601 University Drive, San Marcos, TX, United States of America, tj17@txstate.edu, Shuying Li, Hong-zhong Huang We propose a multi-criteria, performance-based maintenance contract to maximize the utilities of the customer and the supplier under principal-agent model. We prove that supplier’s decision on maintenance time, spares stocking and repair capacity are fully observable to the customer, hence ensuring fully efficient service delivery with no moral hazards. We further show that customers are incentivized to share advance demand information with the supplier for new product acquisition. 2 - A Survey of Condition-based Maintenance Policies for Deteriorating Systems Suzan Alaswad, Assistant Professor, Zayed University, Kalifa City B, Abu Dhabi, United Arab Emirates, Suzan.Alaswad@zu.ac.ae, Yisha Xiang This paper reviews CBM literature highlighting the various stochastic modeling approaches. This paper classifies the CBM models based on the system stochastic degradation model (i.e. whether the degradation state is discrete or continuous) into three deterioration models: discrete, proportional hazard model (PHM), and continuous, and surveys existing CBM models based on this classification for both single and multi unit systems. 3 - Markov Additive Processes for Degradation with Jumps under Dynamic Environments Yin Shu, University of Houston, E206 Engineering Bldg.2, Houston, TX, 77204, United States of America, yinshulx@gmail.com, Qianmei Feng, Edward Kao, Hao Liu, David Coit We use Markov additive processes to integrally handle the complexity of degradation including internally- and externally-induced stochastic properties with complex jump mechanisms. We derive the Fokker-Planck equations for such processes, based on which we derive explicit results for life characteristics represented by infinitesimal generator matrices and Levy measures. The superiority of our models is their flexibility in modeling degradation data with fluctuation under dynamic environments. MD74 74-Room 204A, CC Advanced Maintenance Modeling Sponsor: Quality, Statistics and Reliability Sponsored Session

MD72 72-Room 203A, CC Panel Discussion on Big Data Science –

Opportunities and Challenges Sponsor: Quality, Statistics and Reliability Sponsored Session

Chair: 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 1 - Panel Discussion on Big Data Science - Opportunities and Challenges 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: Soundar Kumara, Liying Cui, Yan Xu, Andrew Kusiak This panel brings experts from academia and industry to discuss the opportunities and challenges in big data science. The panelists are: Dr. Andrew Kusiak, Professor and Chair, The University of Iowa; Dr. Soundar Kumara, Professor, The Pennsylvania State University; Dr. Yan Xu, Senior Manager, Big data optimization group, SAS Institue; Dr. Liying Cui, network improvement manager, Starbucks; ... MD73 73-Room 203B, CC Data Analytics in Manufacturing and Service Industries Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Nan Chen, National University of Singapore, 117576, Singapore, isecn@nus.edu.sg Co-Chair: Kaibo Wang, Associate Professor, Tsinghua University, Department of Industrial Engineering, Beijing, China, kbwang@tsinghua.edu.cn 1 - Modeling Air Quality Data based on Physical Dispersion Processes Xiao Liu, IBM, Singapore, liuxiao@sg.ibm.com In this paper, we investigate a statistical modeling approach based on a commonly used physical dispersion model, called the scalar transport equation. The relationship between the proposed spatial-temporal model and the physical model is well established. The model describes the pollutant concentration by a non- stationary random field with a space-time non-separable and anisotropic covariance structure. 2 - Remaining Useful Life Prediction using Mixed Effects Model with Mixture Prior Distributions Raed Al Kontar, UW Madison, Eagle Heights 301J, Madison, WI, United States of America, alkontar@wisc.edu, Junbo Son, Shiyu Zhou In Modern engineering systems, pre-mature failure has become quite rare. Thus, degradation signals used for prognosis are often imbalanced. Such imbalanced data may hinder accurate remaining useful life prediction especially in terms of detecting pre-mature failures as early as possible. We propose a degradation signal based RUL prediction method to address the imbalance in data. This method captures the characteristics of different groups and provides real time updating of an in-service unit

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