2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - An Empirical Analysis Of Price Dispersion In Electronic Markets
Jin Sik Kim, University of California-Irvine, Irvine, CA, 92617,
United States, , 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,
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,
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.
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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,
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, , 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,
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 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,
, 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, 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,
1 - A Spatial Calibration Model For Quality Prediction
Kaibo Wang, Tsinghua University,
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, , 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,
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,
, 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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