2016 INFORMS Annual Meeting Program

WA21

INFORMS Nashville – 2016

WA18 106A-MCC DMA Data-Driven Models Contributed Session Chair: Nuo Xu, University of Alabama at Birmingham, 5720 11th Avenue South, Birmingham, AL, 35222, United States, nuoxu@uab.edu 1 - Remaining Useful Life Prediction Of Lithium Ion Batteries Using A Novel Degradation Model Fangfang Yang, City University of Hong Kong, Hong Kong, China, fangfyang2-c@my.cityu.edu.hk, Kwok-Leung Tsui Some lithium-ion battery materials show two-phase degradation behavior, such as lithium nick manganese cobalt oxide (NMC) cells. To predict remaining useful life (RUL) for these types of batteries, a model-based Bayesian approach is propose. First, a novel degradation model is developed to capture the degradation trend of NMC batteries. Next, a particle filtering-based prognostic method is incorporated into the model to estimate possible degradation trajectories of the batteries. The effectiveness of the developed method is verified using our experimental data. The results indicate that the proposed prognostic method can achieve high prediction accuracies at an early stage of life. 2 - Process Monitoring And Diagnosis Of Hot Rolled Trip Based On Regression Coefficients Of Batches Fei He, University of Science and Technology Beijing, China, Beijing, China, hefei@ustb.edu.cn First of all, regression model between process parameters and product quality data is established. And then regression coefficients are used for process monitoring and diagnostics. In this paper the model based on partial least squares is bulit between process variables and width of finishing hot rolling, and regression coefficients of all batches are obtained that is used for process monitoring and diagnostics. Experiments on simulated data sets and real data sets show that can effectively locate the important abnormal process parameters. 3 - Data Science And The Liberal Arts Curriculum Anna Engelsone, Maryville College, Maryville, TN, United States, anna.engelsone@yahoo.com This paper draws on over ten years of experience practicing DMA in an industry setting and teaching data science concepts to students ranging from 8th graders to MBAs. Our main interest is in incorporating DMA into the liberal arts curriculum. Liberal arts colleges are uniquely positioned to produce versatile data professionals with the ability to ask the right questions, consider the social implications of their work, and communicate their findings effectively to different audiences. We discuss the challenges of introducing DMA to undergraduates and present examples of in-class exercises, homework problems and research projects suitable for students of different levels and backgrounds. 4 - A Measure Of General Functional Dependence Among Multiple Continuous Variables Nuo Xu, University of Alabama at Birmingham, 5720 11th Avenue South, Birmingham, AL, 35222, United States, nuoxu@uab.edu, Xuan Huang Existing measures in the literature that are specifically concerned with testing and measuring independence between two continuous variables are all based on examining the definition of independence. In a previous paper of ours, we construct a new measure that uses the absolute value of first difference on adjacent ranks of one variable with respect to the other. This measure captures the general functional dependence between two variables. Here, we are presenting the method of generalizing this measure to capture functional dependence among N variables and some preliminary results of its application in variable interaction detection and variable selection.

2 - Unit Commitment With N-1 Security And Wind Uncertainty Kaarthik Sundar, Texas A&M, kaarthik01sundar@gmail.com, Harsha Nagarajan, Miles Lubin, Sidhant Misra, Russell Bent, Line Roald, Daniel Bienstock As wind energy penetration rates continue to increase, a major challenge facing grid operators is the question of how to control transmission grids in a reliable and a cost-efficient manner. The stochasticity of wind forces an alteration of traditional methods for solving the day-ahead unit commitment problem. To address these questions, we present an N-1 Security and Chance-Constrained Unit Commitment that includes the modeling of generation reserves to respond to wind fluctuations and tertiary reserves to account for single component outages. We develop a benders decomposition algorithm to solve the problem to optimality and present a detailed case study on the IEEE RTS-96 three-area system. 3 - Efficient Dynamic Compressor Optimization In Natural Gas Transmission Systems Pascal Van Hentenryck, University of Michigan, pvanhent@umich.edu The growing reliance of electric power systems on gas-fired generation to balance intermittent sources of renewable energy has increased the variation and volume of flows through natural gas transmission pipelines. Adapting pipeline operations to maintain efficiency and security under these new conditions requires optimization methods that account for transients and that can quickly compute solutions in reaction to generator re-dispatch. This talk presents an efficient scheme to minimize compression costs under dynamic conditions where deliveries to customers are described by time-dependent mass flow. Invited: Tutorial Invited Session Chair: Ananda Swarup Das, IBM India Research Labs, India, New Delhi, 1, India, anandas6@in.ibm.com 1 - Mining Qualitative Attributes To Assess Corporate Performance Aparna Gupta, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY, 12180, United States, guptaa@rpi.edu, Ananda Swarup Das, L Venkata Subramaniam, Gagandeep Singh We present an overview of systems and methods to track ongoing events from sources such as corporate filings, financial articles, expert or analyst reports, press releases, customers’ feedback and news articles that have an effect on corporate performance. In this paper we discuss text analytics and sentiment mining approaches to determine quantitative attributes that can be an indicator of corporate performance. For example, strengths, weaknesses, opportunities and threats (SWOT) analysis is a well-known structured planning method widely applied to identify the factors determining success or failure of an enterprise. This analysis can be strongly indicative of the business or financial health of the enterprise. It can provide broader indicators for the firm’s business environment, in terms of ease of doing business in the country, government policies helping (or hurting) business environment. WA21 107A-MCC Chronic Disease Management Sponsored: Health Applications Sponsored Session Chair: Vedat Verter, McGill University, 1001 rue Sherbrooke Ouest, Bronfman Building, Montreal, QC, H3A 1G5, Canada, vedat.verter@mcgill.ca Co-Chair: Michael Klein, McGill University, McGill, Montreal, QC, Canada, michael.klein2@mail.mcgill.ca 1 - Chronic Disease Management And The Role Of Incentives Christian Wernz, Virginia Tech, cwernz@vt.edu, Hui Zhang Chronic diseases can be prevented by changing the behavior of patients and physicians. Incentives are one of the mechanisms to motivate such change. We present a two-player, multi-period model in which patients and physicians jointly decide on prevention activities. The physician-patient interaction is modeled as a general-sum stochastic game with switching control structure. The Health Belief Model (HBM) is incorporated to capture behavioral aspects. We illustrate our modeling approach by applying it to a coronary heart disease cases study. Result show how and to what extent a re-alignment of incentives can improve chronic disease management initiatives. WA20 106C-MCC Mining Qualitative Attributes to Assess Corporate Performance

WA19 106B-MCC Uncertainty in Engineered Networks Sponsored: Computing Sponsored Session

Chair: Russell Bent, Los Alamos National Laboratory, Los Alamos National Laboratory, Los Alamos, NM, 00000, United States, rbent@lanl.gov 1 - Optimal Robust Battery Operation Shuoguang Yang, Columbia University, sy2614@columbia.edu We present formulations, algorithms and computational results on mult-time period problems involving battery operation. In this context, batteries are used to compensate for errors in forecasts for renewable power generation. We model uncertainty sets using the uncertainty budgets model, and we describe efficient implementations. Joint work with D. Bienstock, G. Munoz and C. Matke.

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