2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - Graph Mining For Alzheimer Disease
Fei Gao, Arizona State University, Tempe, AZ, 85281,
United States, Teresa Wu
Apolipoprotein E (APOE) is a gene considered to be highly correlated with the
risks of having Alzheimer’s disease (AD). In this study, imaging data (T1 and
DWI) of two cohorts of patients: APOE carrier and non-carrier is studied. Brain
network were first generated based on which linear regression on graphic features
such as clustering coefficient, mean degree versus age was conducted. The results
showed there may be differences between the two cohorts. This finding motivated
us further to develop a multi-task feature selection method to identify the critical
sub-graphic regions that can significantly improve the discriminative power using
the new graph features versus age correlation.
3 - Mining Hierarchical Event Labels In Large-scale Eeg Collections
Kay Robbins, University of Texas at San Antonio,
Nima Bigdely-Shamlo
When comparing results from two similar EEG studies, researchers must
manually map event types in one study to those of the other. To address event
mapping and to facilitate large-scale data sharing, we created an event-labeling
scheme called Hierarchical Event Descriptors (HED). HED is a common, extensible
vocabulary, with more detailed tags appearing lower in the hierarchy. We tagged
over 3 million events across 1,127 datasets using the HED tag system. We perform
a cross study analysis by investigating ERP/ERSP patterns calculated by averaging
over data recording trials extracted by matching HED tags. We evaluate the
correlations of these patterns and their relationship to particular tags.
4 - Capturing Dynamics Of Brain Functional Networks Through Data
Driven Techniques
Laleh Najafizadeh, Rutgers University,
The brain is a highly complex dynamic system in which neuronal connections are
continuously formed and dissolved at multiple temporal scales. A challenging
problem in the field of neuroscience has been to find reliable techniques that can
describe such inherently dynamic properties of brain function. One promising
approach to investigate brain’s functional architecture is to study its function at
the network level within the context of functional connectivity. Utilizing this
approach, here, we present data driven frameworks to examine the dynamic
nature of neuronal activity, during the execution of tasks.
TD03
101C-MCC
Recent Developments in Opaque and
Probabilistic Selling
Invited: Business Model Innovation
Invited Session
Chair: Tingliang Huang, Carroll School of Management, Boston
College, Chestnut Hill, MA, United States,
1 - Selling Through Priceline? On The Impact Of
Name-your-own-price In Competitive Market
Xiao Huang, John Molson School of Business, Concordia
University, Greys Sosic,
Gregory E Kersten
We study how competitive sellers with substitutable goods may sell their products
(1) as regular goods, through a direct channel at posted prices, and/or (2) as
opaque goods, through a 3rd-party NYOP channel. We establish a stylized
framework with two competing sellers, an intermediary NYOP firm, and a
sequence of customers. We characterize customers’ optimal purchasing/bidding
decisions and sellers’ dynamic pricing equilibrium, and then illustrate the impact
of inventory and time on prices, profits and channel strategies via numerical
studies. Interestingly, although competing sellers seldom benefit from the
existence of NYOP, it is possible that some seller(s) may adopt it in equilibrium.
2 - Opaque Selling And Last-minute Selling: Revenue Management In
Vertically Differentiated Markets
Hang Ren, School of Management, University College London,
London, United Kingdom, ,
Tingliang Huang
Firms in many industries often reduce the price of products/service at the end of
the selling season to dispose of unsold inventory/capacity. This last-minute selling
induces consumers to wait for sales and thus lowers the regular price. To
overcome the problem, many firms switch to opaque selling, i.e., mixing different
types of leftovers and sell them as one type of product. We study the performance
of last-minute selling and opaque selling in vertically differentiated markets, and
find that opaque selling is less efficient in cleaning up leftovers, and the firm may
switch to last-minute selling when high demand becomes more likely. Both
results are contrary to the horizontal differentiation case.
3 - Vertical Probabilistic Selling Under Competition:
The Role Of Consumer Anticipated Regret
Dongyuan Zhan, University College London, London, WC1E 6BT,
United Kingdom, Yong Chao, Lin Liu
We study probabilistic selling with vertically differentiated products when firms
compete and consumers anticipate the potential post-purchase regret raised by
obtaining the inferior products. Intuitively, anticipated regret hurts the
attractiveness of probabilistic selling. However, we find that probabilistic selling
can be more profitable, and more likely to arise with anticipated regret than
without it. That is due to the “reverse quality discrimination” (perceived quality
of the random product is non-increasing in consumer type), which increases the
perceived differentiation at the competition margin, and maintains the random
products attractive to the infra-marginal consumers.
TD04
101D-MCC
Joint Session QSR/ENRE: Data-driven Modeling and
Analytics in Wind Power Systems
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Arash Pourhabib, Oklahoma State University,
322 Engineering North, Stillwater, OK, 74078, United States,
Co-Chair: Eunshin Byon, University of Michigan, College Station, MI,
United States,
1 - Extreme Loads Analysis: Extrapolation And Importance Sampling
Peter Graf, National Renewable Energy Laboratory,
15013 Denver West Parkway, Golden, CO, 80401, United States,
Assessing wind turbine extreme loads requires estimating tails of probability
distributions to construct “exceedance plots” of probability versus peak loads in a
10 minute simulation corresponding to a once-in-50-years event. The IEC
standard contains a prescription for how to estimate these loads. Many find it
unsatisfying because it relies on extrapolation to achieve the 10E-8 level.
Alternative methods based on more strategic sampling of conditions are promising
because they may allow for direct statistical realization of the extreme loads. This
paper compares the existing IEC approach to one based on Importance Sampling.
2 - Space-time Modeling Of Asymmetric Local Wind Fields
Ahmed Aziz Ezzat, Texas A&M University, College Station, TX,
United States, Mikyoung Jun, Yu Ding
Local wind fields refer to the wind dynamics in a space-time domain composed of
a dense grid of locations with close space-time proximity. A typical application is
modeling wind stream behavior using measurements at wind turbines on a wind
farm. Existing literature tends to overlook space-time interaction by imposing
separable, symmetric models. Our analysis suggests that local wind dynamics are
asymmetric in nature, and this asymmetry pattern is dynamically changing due to
alternation of dominant winds. Modeling such physical phenomenon can have a
vital impact on our understanding of local wind dynamics, enabling better
forecasts and robust control strategies in wind energy applications.
3 - Data-driven Stochastic Transmission Expansion Planning
Ali Bagheri, Oklahoma State University,
Due to the significant improvements of power generation technologies and
replacing traditional power plants with renewable ones, the generation portfolio
will experience dramatic changes. The uncertainty of renewable energy and their
sitting call for economic plans for expanding the transmission capacities. In this
study, by learning from the historical data, we first construct a confidence set for
the unknown distribution of the uncertain parameters. Then, we develop a two-
stage data-driven transmission expansion framework, by considering the
worst-case distribution within the constructed confidence set. To tackle the model
complexity, we propose a decomposition framework.
4 - Data-driven Approach For Wake Effect Analysis: Generalization
To All Wind Directions
Mingdi You, University of Michigan,
Eunshin Byon, Judy Jin
Utility-scale wind farms consist of a large number of turbines. To improve the
power generation efficiency of turbines, accurate quantification of power
generations of multi-turbines is critical in wind farm design and operations. One
challenging issue is that the power outputs of multiple turbines are different
because of complex interactions among turbines, known as wake effects. In
general, downstream turbines tend to produce less power than upstream turbines.
When wind direction changes, such wake correlations among turbines also
change. This study proposes a new statistical approach that quantifies the wake
effects on power generations under different wind directions.
TD03