Informs 2015

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INFORMS Philadelphia – 2015
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4 - A Hybrid Computational Method based on Convex Optimization
for Outlier Problems
Fatma Yerlikaya Ozkurt, Middle East Technical University,
Institute of Applied Mathematics, Ankara, Turkey,
, Aysegul Askan,
Gerhard Wilhelm Weber
Statistical modeling plays a central role for any prediction problem of interest.
However, predictive models may give misleading results when the data contain
outliers. In many applications, it is important to identify and treat the outliers
without direct elimination. To handle such issues, a hybrid computational method
based on conic quadratic programming is introduced and employed on
earthquake ground motion data set. Results are compared against widely-used
ground motion prediction models.
WD35
35-Room 412, Marriott
Global Issues II
Contributed Session
Chair: Feifan Wang, Zhejiang University, No.38, Zheda Road,
Hangzhou, China,
1 - A Framework of Social Recommender System Combining Social
Network and Sentiment Analysis
Donghui Yang, Southeast University, Sipailou 2#, Nanjing, China,
In this contribution, we propose a new framework for a social recommender
system based on both network structure analysis and social context mining.
Exponential random graph modelsand sentiment similarities are used to make the
social recommender system much more precise and to satisfy users’ psychological
preferences.The recommendation results of diabetes accounts of Sina Weibo show
that our method outperforms other social recommender systems.
2 - Decision Tree Based Method for Prediction of Preventable
Readmissions in Acute Myocardial Infarction
Andres Garcia-Arce, University of South Florida, 4202 E. Fowler
Avenue, ENB 118, Tampa, FL, 33620, United States of America,
Jose L. Zayas-Castro, Florentino Rico,
Shuai Huang
Preventable readmissions are recognized as a target for quality improvement. The
US government implemented economic penalties to decrease the preventable
readmissions, which leads stakeholders to improve to avoid penalties. The
literature show several statistical models that help hospitals understand
readmissions risk in their institutions, however, these models usually fail to
achieve a good discriminatory power. A random forest-based predictive model is
studied, achieving an AUC=0.7494.
3 - Assess Care Coordination by Multi-criteria Ranking
Wei Liu, Purdue University, Industrial Engineering, West
Lafayette, IN, United States of America,
Ping Huang, Steven Landry
Care coordination reflects the quality of care and impacts the patient outcome. It
remains a challenge to quantify the interactions among providers from various
services as well as the relationships among patients and providers. We use a novel
method of multi-criteria ranking to assess the care coordination under
consideration. It may aid decision makers to identify appropriate interventions to
improve care.
4 - Performance of Different Generalized Propensity Methods in
Evaluating Multi-arm Nonrandomized Study
Feifan Wang, Zhejiang University, No.38, Zheda Road, Hangzhou,
China, Haomiao Jin, Zhengxiao Wang
Generalized propensity score (GPS) is a widely used approach to adjust the
inherent bias existed in multi-arm nonrandomized study. A simulation study is
conducted to assess the performance of four GPS methods: regression adjustment,
matching, stratification, and inverse probability weighting. Practical implications
are discussed and a case is provided.
WD36
36-Room 413, Marriott
Humanitarian Operations Management Applications
Sponsor: Public Sector OR
Sponsored Session
Chair: Alfonso Pedraza-Martinez, Assistant Professor, Indiana
University, 1309 E 10th Street, Bloomington, IN, 47405, United States
of America,
1 - Fast and Frugal Disaster Response? Decisions in Typhoon Haiyan
Tina Comes, University of Agder, Postboks 509, Grimstad, 4898,
Norway, Bartel Van De Walle
In the response to sudden-onset disasters, humanitarian organisations operate
under trying conditions in which response targets evolve as information on the
actual impact becomes available. The pressing humanitarian needs require fast
decision making, including decisions on warehouse locations and the allocation of
relief items, yet with little time for more than frugal analyses. In this presentation,
we study the response to Typhoon Haiyan which struck the Philippines in
November 2013.
2 - Estimating and Incorporating Deprivation Costs into Humanitarian
Logistic Models for Relief Response
Victor Cantillo, Associated Professor, Unirversidad del Norte,
Km 5 via Puerto Colombia, Barranquilla, Colombia,
Nathalie Cotes, Luis Macea,
Ivan Serrano
This research allows mathematical formulations using discrete choice modelling to
quantify externalities associated to the lack of access to critical commodities in the
aftermath of a disaster. Thus the estimated deprivation cost function is explicitly
incorporated into the objective function of facility location models for
prepositioning supplies, which attempt to minimize the total social costs, as
determined by both operational and social considerations. The models are applied
to a real case.
3 - Assembling High Quality and Timely Information for Humanitarian
Organizations from Social Media
Eunae Yoo, Arizona State University, P.O. Box 874706, Tempe,
AZ, 85287, United States of America, ,
Mahyar Eftekhar, Elliot Rabinovich, Bin Gu
To support operational decision making, humanitarian organizations require high
quality and timely data. We investigate how such information can be extracted
from social media using automated data mining mechanisms that rapidly process
data. The effectiveness of data mining mechanisms are tested using a sample of
Twitter data. Our results help shed light on what constitutes high quality
information for humanitarian organizations and how it can be speedily obtained
from social media.
4 - Humanitarian Funding in a Multi-donor Market with
Donation Uncertainty
Alfonso Pedraza-Martinez, Assistant Professor,
Indiana University, 1309 E 10th Street, Bloomington, IN, 47405,
United States of America, Arian Aflaki
We analyze the trade-off between earmarked funding and operational
performance. If a Humanitarian Organization (HO) allows donors to earmark
their donations, HO’s expected funding increases but its operational efficiency
decreases. We use the Scarf’s minimax approach and the newsvendor framework,
and calibrate our model using data from 15 disasters.
WD37
37-Room 414, Marriott
Health Care Strategy and Policy II
Contributed Session
Chair: Neil Desnoyers, Instructor, Saint Joseph’s University,
133 Green Valley Rd, Upper Darby, PA, 19082, United States of
America,
1 - Revenue-based Booking Policy for Clinic Appointment with
Overbooking Considering Patient No-shows
Jiafu Tang, Chair Professor, Dean, Dongbei University of Finance
and Economics, School of Management Science, and Engineering,
Dalian, 116025, China, Pingping Cao,
Xuanzhu Fan
In this paper, an advanced clinic access system is designed. We formulate a
Markov Decision Process model with its extension considering regular patients’
no-shows and patient choice to improve clinic revenue by overbooking same-day
patients, and then to improve patient satisfaction by allowing patients to choose
either a same-day or a scheduled future appointment. Numerical experiments and
analysis are made finally.
WD35