2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - Data Mining For Result Prediction In Sports
Kyuhan Lee, Seoul National University, Seoul, Korea,
Republic of,
Jinsoo Park, Buomsoo Kim
The expansion of sports betting has extensively contributed to the increase of
public interest on sports result prediction. In academia, as statistical data
respecting sports games are readily accessible, abounding research has been
conducted regarding the subject. In this paper, unlike the past studies focusing on
limited types of data, we use a comprehensive set of data, including statistical data
as well as text data, to enhance the accuracy of sports result prediction. We expect
that our prediction model produces a preferable outcome comparing to the
models of previous research.
4 - Predicting Users’ Continuous Participation In Online Health Virtual
Community: Demographic And Content Cues
Yanyan Shang, Dakota State University, Madison, SD,
United States, , Jun Liu, Iljoo Kim
Online health community (OHC) is a platform where people with similar health
conditions gather virtually to ask questions, share experiences, provide support,
and exchange healthcare knowledge. To be effective, the OHCs have to maintain
active continuance participation from their users. The purpose of this study is to
identify factors that affect the users’ continuance participation. Specifically, we
attempt to use data analytics techniques to identify the demographic and content
cues that affect the users’ continuance participation. The findings of our research
help community managers deploy various strategies to encourage the
continuance participation of different types of members.
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103A-MCC
Undergraduate OR Prize - II
Invited: Undergraduate Operations Research Prize
Invited Session
Chair: Pavithra Harsha, IBM Research, 1101 Kitchawan Road,
Room 34-225, Yorktown Heights, NY, 10598, United States,
1 - Hot Sales Logistics Optimization For ET
Ba ak Erman, Bilkent University, Ankara, Turkey,
Zeynep Yaprak Be ik, Deniz Berfin Karakoç, Umut Müdüro Lu,
Yekta Jehat Mizrakli, Egehan Yanik
ET is one of the leading food manufacturer in Turkey. Additonal to standard
distribution system, ET has a local distribution system that enables trucks to visit
smaller retailers and pursue hot sales. The aim of the project is to increase the
efficiency of hot sales where demand by the retailers are better satisfied. The
delivery route is divided into two: route from the main depot, which is far from
customers, to the customers, and the route between customers. This project aims
to maximize the utility of time spend in routes by assigning customers to trucks
and identifying depot locations.
2 - Regularized Linear Regression via Robust Optimization Lens
Hari Bandi, Massachusetts Institute of Technology, Cambridge,
MA, United States, Garud Iyender, Vineet Goyal
There has been research in recent years to understand why regularized linear
regression methods work well in the presence of noise. This problem has been
approached by establishing relationship between robust optimization and
regularized linear regression methods. In this work, we seek to understand the
same for general loss functions used widely in Statistics, Machine Learning and
Econometrics literature and we propose principled approaches to select
regularization functions in order to optimally balance the bias-variance trade-off
in regularized regression.
3 - On Comprehensive Mass Spectrometry Data Analysis For Quality
Assessment Of Biological Samples
Sameer Manchanda, Purdue University, West Lafayette, IL,
United States, Mikaela Meyer, Nan Kong, Qianqian Li, Yan Li
Mass spectrometers have become promising instruments to acquire proteomic
information, creating a need for a data analysis platform for classification of mass
spectra and identification of important biomarkers. To meet this need, we present
a comprehensive pattern recognition platform for spectrum preprocessing and
classification. In a case study, the platform achieves higher than 90% sensitivity
and specificity in distinguishing rat blood samples stored for different amounts of
time and derives fingerprint patterns of serum proteins that are strongly
associated with the sample classification.
4 - Allocating Countermeasures To Defend Water Distribution
Systems Against Terrorist Attack
Jacob Monroe, North Carolina State University, Raleigh, NC,
United States,
Elizabeth Ramsey, Emily Zechman Berglund
An agent-based model is developed to simulate the attack and defense of a water
distribution system to analyze security resource allocation strategies for protecting
against chemical contamination events. A single period attacker-defender game is
simulated, in which an attacker seeks to contaminate a system node, and a group
of defenders seek to minimize the public health impact from attack. Terrorist
decisions are simulated using a multi-attribute utility function. The utility
manager assigns personnel and security equipment to nodes using one of three
security resource allocation strategies.
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103B-MCC
Energy and Environmental Policy
Sponsored: Energy, Natural Res & the Environment I
Environment & Sustainability
Sponsored Session
Chair: Yihsu Chen, University of California Santa Cruz,
1156 High Street, M/S SOE3, Santa Cruz, CA, 95064, United States,
1 - The Cost Of Reaching Mexicos Climate Change Goals
Rodrigo Mercado Fernandez, UMass, Amherst, MA,
This paper analyzes the cost of Mexico reaching its climate change emissions
goals, using integrated assessment models, and looks at how this will affect the
electricity generation portfolio. These results are compared with the predicted
impacts that Mexico’s current policies will have on emissions and generation.
Lastly this paper identifies policy changes that could help Mexico reach its long-
term emissions goals for 2030 and 2050.
2 - On The Inefficiencies Of The US Federal Clean Power Plan
Duan Zhang, University of California Santa Cruz, Santa Cruz, CA,
United States, Yihsu Chen, Makoto Tanaka
The performance-based standard under the US federal Clean Power Plan relies on
trading the emission rate credits (ERCs), which represent the equivalent MWh of
energy generated or saved with zero associated CO2 emissions, to equating
marginal abatement costs across generating units. We show theoretically the
equivalence between the ERCs and the traditional mass-based trading when states
are subject to their own performance-based standards. We also identify the
conditions under which the inefficiency of the performance-based standard might
arise, leading to a divergence of permit prices across states. A numerical 3-node
model was built to illustrate our findings.
3 - Feed-in Tariffs Vs. Renewable Portfolio Standards:
The Effect Of Market Power
Mari Ito, Tokyo University of Science, Noda, Japan,
Ryuta Takashima, Makoto Tanaka,
Yihsu Chen
Recently policies for promoting renewable energy, e.g., feed-in tariffs (FIT) and
renewable portfolio standards (RPS) have been introduced in various countries.
In this work, we examine an effect of market power in the electricity market on
FIT and RPS by bi-level model. For lower level, generation outputs for renewable
and non-renewable generators are decided by maximizing their profits whereas
for upper level, the fixed price of FIT and the RPS requirement are derived by
maximizing a social welfare. In addition, we show how the number of firms
affects the fixed price and the requirement.
4 - Tradable Performance-based Co2 Emissions Standards:
Walking On Thin Ice?
Yihsu Chen, University of California Santa Cruz,
Afzal Siddiqui, Makoto Tanaka
US federal Clean Power Plan (CPP) stipulates a state-specific performance-based
CO2 standard and offers considerable flexibility to the states in achieving the
target. We analyze the tradable performance standards and related mass-based
standard when they are subject to imperfect competition by formulating them
either as a complementarity problem or a mathematical program with
equilibrium constraints (MPEC). The MPEC is solved as mixed integer problems
with a binary expansion. We show that while the cross-subsidy inherent in the
performance-based standard that might effectively reduce power prices, it could
in inflate energy demand, thereby rendering permits scarce.
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