2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - Load Forecasting Using Support Vector Machine With
Optimized Parameters
Olufemi A. Omitaomu, Oak Ridge National Laboratory,
Load forecasting is central to most of the energy transaction decisions in power
systems planning and energy markets. Until now, most approaches for forecasting
energy demand rely on monthly electrical consumption data. The emergence of
smart meters is changing the data landscape for electric utility companies, and
creating opportunities for utility companies to collect and analyze energy
consumption data at a much finer temporal resolution. To enhance the estimation
of energy demand at the household and network levels, we present an on-line
accurate support vector regression technique that uses optimized regression
parameters for forecasting real-time energy demand using smart meters data.
4 - Catch Me If You Can: Detecting Pickpocket Suspects From
Large-scale Transit Records
Chuanren Liu, Drexel University,
Massive data collected by automated fare collection (AFC) systems provide
opportunities for studying both personal traveling behaviors and collective
mobility patterns in the urban area. We creatively leverage such data for
identifying thieves in the public transit systems. We develop a thief active tracking
system that identifies pickpocket suspects based on their daily transit records. We
first extract a number of features from each passenger’s daily activities in the
transit systems. Then, we exploit a combination of outlier detection and
classification models to identify thieves, who exhibit abnormal traveling
behaviors.
SC02
101B-MCC
Quality and Statistical Decision Making in Health
Care Applications
Sponsored: Data Mining
Sponsored Session
Chair: Cao Xiao, University of Washington, 3900 Northeast Stevens
Way, MEB, Seattle, WA, 98195, United States,
Co-Chair: Shuai Huang, University of Washington, Seattle, WA, United
States,
1 - Modeling And Analysis Of The Waiting Time Of Rapid Response
Process In Acute Care
Nan Chen, Tsinghua University, Room 615, Shunde Building,
Tsinghua University, Haidian District, Beijing, 100084, China,
Xiaolei Xie, Li Zheng
Improving the efficiency of rapid response process in acute care plays a significant
role to ensure patient safety. We develop an analytical method to evaluate the
waiting time and its variability. We discussed the structural properties and
continuous improvement by adding care providers. A bottleneck indicator is
introduced and a simple approximation formula is obtained. Case study is
introduced to illustrated the application of the method.
2 - Modeling And Prediction Of The Mental Health Conditions Of
Web Users
Qingpeng Zhang, City University of Hong Kong, 1, Hong Kong,
The digital footprints of Web users left on the Web presents important proxies of
their health conditions. In this research, we propose novel machine learning
algorithms to model and predict the mental health conditions of Web users based
on their online activities on social media. The preliminary results show the
potential of using the open source social media data to infer the mental health
conditions of people, and help health providers make better decisions.
3 - Learning Semantics Behind Health Status Disclosure On Twitter
Zhijun Yin, Vanderbilt University, Nashville, TN, 37203,
United States, Bradley Malin
User generated content in social media is increasingly acknowledged as a rich
resource for research into health problems. We in this talk present a framework to
investigate how semantics are related with disclosure routines for 34 health
issues. Our findings show that health issues related with family members, high
medical cost and social support (e.g., Alzheimer’s Disease, cancer, and Down
syndrome) lead to tweets that are more likely to disclose another individual’s
health status, while tweets with more benign health issues (e.g., allergy, arthritis,
and bronchitis) with biological processes (e.g., health and ingestion) and negative
emotions are more likely to contain self-disclosures.
4 - Hospital Operational Health Monitoring: Enabling Organizational
Communication Of Key Indicators And Analytics
Diego A. Martinez, Scott R. Levin, Matthew F. Toerper,
Johns Hopkins University School of Medicine, Baltimore, MD,
Most hospitals have adopted electronic medical records, yet leveraging these data
to optimize hospital operations remains a challenge. Grounded in human-com-
puter interaction and visualization theory, we built a web app to facilitate data
exploration and trend analysis. The app allows users to directly explore big data
and scientifically assess whether or not an intervention is impacting hospital per-
formance. Keeping clinicians and hospital leadership informed about practice
operations can help align them with organizational goals, ultimately leading to
better financial performance.
SC03
101C-MCC
Doing Good with Good OR I
Invited Session
Chair: Karen Smilowitz, Northwestern University,
2145 Sheridan Road RM D239, Evanston, IL, 60208, United States,
1 - The Operational Challenges Of Sharing-Economies:
An Optimal Re-balancing Mechanism For The Bike-Sharing
Industry
Pantelis Loupos, Department of Operations Management, Kellogg
School of Management, Northwestern University, Evanston, IL
60208, Can Urguny
Bike-sharing programs have been gathering momentum, but their expansion
poses operational challenges. We propose a novel solution to the bike re-balanc-
ing problem, that is centered around the actions of the riders instead of utilizing
trucks for re-balancing. Our findings indicate great promise, whose adoption by
bike sharing operators could have a positive impact on the industry.
2 - The Humanitarian Pickup And Distribution Problem
Ohad Eisenhandler, Department of Industrial Engineering, Tel
Aviv University, Tel Aviv, Israel, , Michal Tzur
We address the logistic challenges of food banks, which collect donated food
from suppliers and distribute it to welfare agencies. We model the problem as a
routing – resource allocation problem. Motivated by the activity of Israeli and
American organizations, we introduce an innovative objective function, which
balances equity and effectiveness in this operation, and propose exact and
heuristic solution methods.
3 - Data Analytics For Optimal Detection Of Metastatic
Prostate Cancer
Christine Barnett, Department of Industrial & Operations
Engineering, University of Michigan, 1205 Beal Avenue,
Ann Arbor, MI 48109, , Selin Merdan
We used data-analytics approaches to develop, calibrate, and validate predictive
models to help urologists make prostate cancer staging decisions. These models
were used to design guidelines that weigh the benefits and harms of radiological
imaging. The Michigan Urological Surgery Improvement Collaborative imple-
mented these guidelines which miss less than 1% of metastatic cancers while
reducing unnecessary imaging by more than 40%.
SC04
101D-MCC
Gas-Power Market Integration
Sponsored: Energy, Natural Res & the Environment,
Energy I Electricity
Sponsored Session
Chair: Robert Brooks, President, RBAC Inc, 14930 Ventura Blvd. Ste.
210, Sherman Oaks, CA, 91403, United States,
1 - Analysis Of Gas / Electric Integration And Coordination In The
Eastern Interconnection Of The United States And Canada
Sara Wilmer, Levitan & Associates, Inc.,
Levitan & Associates has conducted recent analyses of gas-electric integration and
coordination on behalf of the Eastern Interconnection Planning Collaborative and
the Department of Energy. These analyses examined whether future electric
sector demand for natural gas will be able to be accommodated by the available
natural gas infrastructure as renewable penetration expands and coal-fired
resources are retired. This case study will describe the modeling tools and
integrated modeling framework used to conduct the work, and challenges faced
both in the representation of real-world gas and electric systems in the selected
modeling tools and in the integration of the different modeling tools.
SC04