2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - Assigning Students To Schools To Minimize Socioeconomic
Variation Between Schools
Richard Forrester, Associate Professor of Mathematics, Dickinson
College, College and Louther Streets, Carlisle, PA, 17013, United
States, Kevin Hutson, Elizabeth Bouzarth
Numerous studies have found that a student’s academic achievement is as much
determined by the socioeconomic composition of their school as their own
socioeconomic status. In this talk we provide a methodology for assigning
students to schools so as to balance the socioeconomic compositions of the schools
while taking into consideration the total travel distance. Our technique utilizes a
bi-objective general 0-1 fractional program that is linearized into a mixed 0-1
linear program which can be submitted directly to a standard optimization
package. As a test case for our approach we analyze data from the Greenville
County School District in Greenville, South Carolina.
3 - Consumer's Preferences Modeling For Rail Transportation
In Qatar
Rana Sobh, Qatar University, Doha, Qatar; ,
Belaid Aouni
The increase in traffic congestion, road safety and pollution have led Qatar to
improve the existing public transportation system and introduce Doha Metro.
This shift in public transportation requires changes in consumers’ perceptions
about rail transportation. The aim of our paper is to predict the factors that may
impact consumers’ behavior and their preferences in choosing transportation sys-
tems. Moreover, our study aims to develop a better understanding of the rail
transportation mode adoption in Qatar and provides some recommendations to
the policy makers in Qatar Rail.
MD65
Mockingbird 1- Omni
Data Analytics and Machine Learning
Sponsored: Information Systems
Sponsored Session
Chair: Sriram Somanchi, University of Notre Dame, 344 Mendoza
College of Business, Notre Dame, IN, 46556, United States,
1 - Analytical And Empirical Modeling Of Complementarities In
An Online Advertising Supply Chain
Changseung Yoo, The University of Texas at Austin, Austin, TX,
United States, , Anitesh Barua, Genaro Gutierrez
We examine channel structures and pricing models in an online advertising
supply chain using a proprietary dataset. We develop analytic as well as structural
econometric models that enable us to model interactions between the channel
structures/pricing models, and quantify synergy effects between them. To the best
of our knowledge, our study takes the first step of analyzing details of an online
advertising supply chain. Moreover, while the extant literature emphasizes
choosing between pricing models, we demonstrate that using multiple models in
concert yields higher overall profitability due to spillover effects and strategic
complementarities among the pricing schemes.
2 - Predicting Hotel Revenue Using Hotel Latent Quality
Uttara Madurai Ananthakrishnan, Carnegie Mellon University,
Online reviews have become a major source of information for consumers in the
past decade and influence various aspects of e-commerce such as purchase
decisions and product sales in a variety of settings. Most of the work in this field
has focused on numerical ratings to understand the impact of online reviews on
sales. In our paper, we use a novel topic-modeling technique on a large dataset of
online reviews of hotels and study if topics obtained from this technique provide
a better representation of a hotel’s quality. We then analyze how each hotel’s
quality evolves over time and predict the changes in hotel revenue using such
topics.
3 - SkillR: Personalized Skill Recommendations Using Joint Bayesian
Member-job Clustering
Abhinav Maurya, Carnegie Mellon University, Pittsburgh, PA,
United States, Rahul Telang, Sai Sundar
Skill gaps in various sectors of the economy are considered to be major problems
facing economies today. Increasing the productivity of a member of the workforce
depends on recommending skills whose acquisition will yield the highest utility
gains for the member. We present SkillR - a skill recommendation algorithm that
employs a joint Bayesian clustering model to match members to other similar
members as well as relevant jobs, and to identify the top skills that provide the
maximum utility gains to a member. Our evaluation suggests that SkillR leads to
orders of magnitude improvement in the job propensity of recommended skills
compared to a traditional collaborative filtering system.
4 - Does Government Surveillance Give Twitter The Chills?
Sriram Somanchi, University of Notre Dame, ,
Laura Brandimarte, Edward McFowland, Uttara Madurai
Ananthakrishnan
Since Snowden’s revelations regarding mass surveillance programs implemented
by the NSA, Government surveillance has garnered huge attention. The research
community has attempted to estimate the “chilling effects” of surveillance, the
tendency to self-censor. Until now, such effects have been estimated using either
the search terms, Wikipedia articles, or survey data. In this work, we propose a
new method in order to test for chilling effects in online social media. We use
large Twitter dataset and propose the use of new statistical machine learning
method in order to detect anomalous trends in user behavior after Snowden’s
revelations made users aware of existing surveillance programs.
MD66
Mockingbird 2- Omni
Computer Experiments and Uncertainty
Quantification
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Ying Hung, Rutgers State University of New Jersey, Piscataway,
NJ, United States,
1 - Robust Parameter Design Using Computer Experiments
Roshan Vengazhiyil, Georgia Institute of Technology,
Space-filling designs, commonly used in computer experiments, try to spread out
points uniformly in the experimental region. However, when the objective is to
achieve robustness against noise factors, uniformity is no longer needed in the
space of noise factors. This is because noise factors usually follow nonuniform
distributions such as normal distribution. It makes more sense to place points in
the high probability regions where more “actions” take place. Unfortunately,
nonuniform points in the experimental region can lead to problems in model
fitting. In this article we propose novel design and modeling strategies to deal
with these issues.
2 - Invariance-preserving Emulation For Computer Models,
With Application To Structural Energy Prediction
Peter Qian, University of Wisconsin - Madison,
Simulation models with invariance properties appear in material science, physics,
biology and other fields. Standard emulation methods such as Gaussian process
regression cannot accommodate input invariance and thus do not work for this
new problem. We will propose a kernel-based emulation method to preserve
invariance in inputs. The method employs a direct graph representation and
equivalence relations to characterize relabeling invariance. The effectiveness of
the proposed method is illustrated by using several examples from material
science.
3 - A Sequential Maximum Projection Design Framework For
Computer Experiments With Inert Factors
Shan Ba, The Procter & Gamble Company, Cincinnati, OH,
United States,
Many computer experiments involve a large number of input factors, but many of
them are inert and only a subset are important. In this talk we present a new
sequential design framework which can accommodate multiple responses and
quickly screen out inert factors so that the final space-filling design is close to
optimal with respect to the active factors. The new approach does not require
prescribing the total sample size, and under the presence of inert factors, it can
lead to substantial savings in simulation resources. Even if all the factors are
important, the proposed sequential design can still achieve similar overall space-
filling property compared to a maximin LHD optimized in a single stage.
MD66