2016 INFORMS Annual Meeting Program

TA66

INFORMS Nashville – 2016

2 - An Empirical Analysis Of Price Dispersion In Electronic Markets Jin Sik Kim, University of California-Irvine, Irvine, CA, 92617, United States, jinsk6@uci.edu, Vijay C Gurbaxani Theory predicts that the price of homogeneous products at online retailers will exhibit low price dispersion; yet, there is empirical evidence to the contrary. This paper investigates price dispersion in homogeneous product markets based on uncertainty theory. We examine two types of uncertainty: seller and product uncertainty. We collect data in three product categories: search, experience, and credence goods. Our results show higher product uncertainty is consistent with higher price dispersion. Sellers with stronger reputations can set higher prices in product markets with higher uncertainty, resulting in price dispersion, but not otherwise. 3 - Product Upgrades With Innovation Uncertainty Yiwei Wang, University of California-Irvine, derekw7@uci.edu Firms usually upgrade their products by introducing an innovative attribute. This article looks at how a profit-maximizing firm design and position such upgraded versions, when products with traditional attribute has been supplied in the market. We specifically study the quality design and configuration strategy in the presence of innovation uncertainty, based on a product line selection framework. 4 - The Impact Of Digitization On Optimal Content Pricing Strategy Ran Zhang, University of California-Irvine, ranz2@uci.edu The widespread adoption of the Internet and digital technologies has transformed the distribution and consumption of information goods. We develop a parsimonious model to study pricing strategies of a publisher who offers information good in dual medium and in bundled medium. We develop optimal pricing strategies and show that while offering bundle of mediums and digital medium only (partial mixed bundling) is optimal under a wider range of market conditions, offering digital medium only is optimal under other market conditions. Offering information good or content in physical medium and in digital medium is not optimal as long as the two mediums are partial substitutes. TA66 Mockingbird 2- Omni Data Analytics and Reliability in Energy/Smart Grids Sponsored: Quality, Statistics and Reliability Sponsored Session Chair: Ramin Moghaddass, University of Miami, McArthur Engineering Building, Miami, FL, 33146, United States, raminm@mit.edu 1 - Wind Turbine Wake Effects: Characteristics And Impacts On Wind Power Generation Hoon Hwangbo, Texas A&M University, Dept. of Industrial & Systems Engineering, 3131 TAMU, College Station, TX, 77843- 3131, United States, hhwangbo@tamu.edu, Andrew L Johnson, Yu Ding When a wind turbine operates, rotating blades not only consume energy available in wind but also generate some turbulence, both changing characteristics of downstream wind thereby affecting power generation of downstream wind turbines. This phenomenon is referred to as wind turbine wake, and its effect on power performance is known to be significant. In this study, we observe characteristics of the wake effects from actual wind turbine data and quantify the effects on power performance of wind turbines. 2 - Statistical Monitoring Of Data Attacjs In Smartgrids George Michailidis, University of Florida, gmichail@ufl.edu Data attacks on the distribution network in SmartGrids have the potential to destabilize the power network, as well as impact the consumption patterns of electricity consumers.In this work, we provide an overview of such attacks and develop a statistical framework for their detection. The developed methodology is illustrated on synthetic and real data traces. 3 - Opportunistic Condition Based Maintenance Optimization For Offshore Wind Farm Sanling Song, Postdoc, Rutgers University, 33 Livingston Ave, Room 250, New Brunswick, NJ, 08901, United States, lamusesi38sanling@gmail.com, Frank A Felder, David W Coit Operation and maintenance cost for offshore wind farm can be 5-10 times higher than the cost for on-land wind farm. In this paper, opportunistic condition-based maintenance optimization model is developed. Two objectives we are interested in are wind farm maintenance cost and wind turbine availability or uptime. Genetic algorithm considering uncertainty is conducted, which is especially challenging because life experience for each component in the wind turbine is uncertain. Probabilistic Pareto frontier rather than deterministic Pareto front is obtained.

4 - Robust Optimization Based Power System Restoration For Incorporating Large-scale Wind Farms Amir Golshani, University of Central Florida, Orlando, FL, United States, amir.golshani@knights.ucf.edu, Wei Sun, Qipeng Zheng This presentation provides a novel two-stage optimization model for a faster and reliable power system restoration. The robust optimization approach is employed to immunize the solution against all possible realizations of wind uncertainties. With mixed-integer optimization in the inner-level problem the KKT condition cannot be directly applied. Thus, we adopt the column-and-constraint generation (C&CG) algorithm to solve the two-stage robust optimization problem. The proposed strategy can assist system operators to accomplish the restoration tasks accurately and harness wind energy more efficiently. TA67 Mockingbird 3- Omni IEEE T-ASE Invited Session III Sponsored: Quality, Statistics and Reliability Sponsored Session Chair: Jingshan Li, University of Wisconsin-Madison, 1513 University Ave, Madison, WI, 53706, United States, Jingshan.li@wisc.edu 1 - A Spatial Calibration Model For Quality Prediction Kaibo Wang, Tsinghua University, kbwang@tsinghua.edu.cn The anisotropy of a carbon nanotube (CNT) film, which is a spatially distributed quality index, is difficult to measure in practice due to metrology and cost constraints. However, the anisotropy is highly correlated with the height of the CNT array, which can be measured in a much easier and more cost-effective way. In this talk, we propose a spatial model for predicting the anisotropy using the height. The model takes the spatially distributed two-dimensional (2D) height as an input and provides a predicted anisotropy distribution in a 2D space. If the anisotropy measures are obtained, the model can provide a more accurate prediction. 2 - Estimating Clearing Functions For Production Resources Using Simulation Optimization Reha Uzsoy, NC State University, ruzsoy@ncsu.edu, Baris Kacar We implement the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, to estimate clearing functions (CFs) that describe the expected output of a production resource as a function of its expected workload from empirical data. A simulation model of a scaled-down wafer fabrication facility is used to generate the data and evaluate the performance of the CFs obtained from the SPSA. 3 - A BDD-based Approach For Designing Maximally Permissive Deadlock Avoidance Policies For Complex Resource Allocation Systems Spyros Reveliotis, Georgia Tech, Atlanta, GA, United States, spyros.reveliotis@isye.gatech.edu, Zhennan Fei, Sajed Miremadi, Knut Akesson The maximally permissive deadlock avoidance policy (DAP) for complex resource allocation systems (RAS) can be implemented through the identification and storage of a set of critical states of the underlying RAS state-space, known as minimal boundary unsafe states. This paper presents a symbolic approach, based on binary decision diagrams (BDDs), for efficiently retrieving the (minimal) boundary unsafe states from the underlying RAS state- space. Numerical experimentation demonstrates that the proposed method enables the deployment of the maximally permissive DAP for RAS with complex structure and large state- spaces with limited time and memory requirements. 4 - A Dynamic Control Algorithm For Distributed Feedback Control For Manufacturing Production, Capacity, And Maintenance Seokgi Lee, Assistant Professor, University of Miami, 1251 Memorial Drive 281, Coral Gables, FL, 33146, United States, sgl14@miami.edu, Vittaldas V. Prabhu We propose a dynamic algorithm for distributed feedback control which unifies the functions of production and maintenance scheduling at the shop floor level, and machinery capacity control at the CNC level, which are usually considered in isolation in practice. A continuous-time control theoretic approach is used to model dynamics of these three functions in a unified manner, considering stochastic machine failures and a corresponding maintenance interval. Theories of nonlinear control and discontinuous differential equations are used to analytically predict the system dynamics including the resulting discontinuous dynamics.

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