Informs 2015

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INFORMS Philadelphia – 2015
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2 - Baseball- Discovering the Moneyball Effect
Sean Barnes, University of Maryland, 4352 Van Munching Hall,
University of Maryland, College Park, MD, 20742, United States
of America, , Margret Bjarnadottir
This case uses player evaluation and personnel decision-making in the Major
League Baseball to introduce many of the key steps of data mining projects. The
data mining process is a unique combination of art and science, and teaching the
art of data mining is challenging to do in a standard classroom setting with small
data sets. The goal of this case is to move beyond the simple “cookie cutter” data
sets, and introduce students to the challenges of dealing with real data to answer
important questions, as well as introduce or reinforce multiple data mining
methods. The case builds on a very rich data set collected by the authors, which
allows for students or groups of students to arrive at very different answers to the
same question. For example, what is the best predictive model? Which players
should be pursued?
3 - Medication Waste Reduction in an In-hospital Pharmacy:
A Case that Bridges Problem Solving between a
Traditional Case and an Industry Project
Gregory Dobson, University of Rochester, University of Rochester,
Rochester, NY, United States of America,
Vera Tilson
This Operations Management case describes a waste-reduction project in a
compounding pharmacy in a hospital. Every day, pharmacy technicians prepare a
large number of patient-specific medication doses and then deliver these doses to
various hospital units. With the rising cost of medications, pharmacy managers
become concerned that a significant number of compounded medication doses
are not administered to patients and are subsequently wasted. The students are
asked to quantitatively evaluate proposed changes to the compounding and
delivery process and to estimate savings from process reconfiguration. Two large
datasets are provided with the case to facilitate hypothesis generation regarding
probable causes of waste and to analyze proposed changes. The analysis will
deepen students’ spreadsheet skills as well as mathematical modeling of inventory
problems. The case is presented in parts, and it is discussed over one and a half
class meetings – simulating the steps of a field project: interviewing a client,
framing the client’s problem, formulating a data request, and then analyzing the
data and delivering a recommendation. This case has been used in a core MBA
operations management class; it could also be used in a health-care operations or
in a business modeling course.
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61-Room 111B, CC
Dimensionality Reduction Techniques for
Generation Capacity Expansion Problems with
Intermittent Resources
Sponsor: ENRE – Energy II – Other (e.g., Policy, Natural Gas,
Climate Change)
Sponsored Session
Chair: Fernando de Sisternes, Argonne National Laboratory - MIT
Energy Initiative, 77 Massachusetts Avenue, E19-341, Cambridge, MA,
02139, United States of America,
1 - On the Temporal Resolution of Electric Sector Capacity
Expansion Models
James Merrick, Stanford University, Huang Engineering Center,
Stanford, CA, 94305, United States of America,
When hours are assumed to be independent, we can capture hourly information
by removing similar hours and weighting a representative. Applying hierarchical
clustering to a sample dataset, I show that while the variability of demand can be
captured in the order of 10 hours, including wind and solar resources warrants in
the order of 1000. A similar analysis is undertaken for days and weeks. I conclude
with how the approach can reduce resolution further using information about
problem structure.
2 - A Robust Method to Choose Representative Weeks in Renewable
Generation Capacity Expansion Problems
Fernando de Sisternes, Argonne National Laboratory - MIT
Energy Initiative, 77 Massachusetts Avenue, E19-341, Cambridge,
MA, 02139, United States of America, ,
Ignacio Núñez
We propose a new week selection method for capacity expansion formulations
with unit commitment constraints that is robust to the renewable capacity in the
system. This method enables the use of such formulations in determining the
optimal amount of thermal and renewable capacity. The proposed method selects
a week combination with hourly resolution that represents the energy below the
net load duration curve and its inter-temporal variability across different
renewable capacity scenarios.
3 - Optimization-based Method for Scenario Reduction in Generation
Expansion Models
Ignacio Núñez, Research Assistant Professor, University of the
Andes, Av. Monseñor Alvaro del Portillo 12.455, Las Condes,
Santiago, RM, 7620001, Chile, Fernando de
Sisternes
Including intermittent generation in generation expansion models requires a
detailed representation of the short-term operation of the system, increasing
dramatically the dimensionality of the problem. We present a mixed integer linear
model that selects a specific number of representative scenarios (e.g. days or
weeks) and weights to minimize the maximum error in representing the net load
duration curve for different capacities of solar photovoltaic and wind power.
4 - Modeling a Paradigm Shift: Distributed vs. Centralized Options in
Electricity Capacity Planning
Jesse Jenkins, PhD Student And Researcher, MIT Engineering
Systems Division and MIT Energy Initiative, 77 Massachusetts
Avenue, Cambridge, MA, 02139, United States of America,
Distributed energy resources such as solar PV, combined heat and power,
batteries, and demand response, may compete with conventional power plants in
the provision of electricity services. This research employs state-of-the-art
dimensionality reduction techniques to ensure the computational tractability of a
generation capacity expansion model that considers tradeoffs between distributed
and centralized energy options, including impacts on network costs, losses,
congestion, and system services.
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62-Room 112A, CC
Modeling, Analysis, and Management of Water-
Centric Systems
Sponsor: ENRE – Environment I – Environment and Sustainability
Sponsored Session
Chair: Nagesh Gavirneni, Associate Professor, Cornell University, 325
Sage Hall, Ithaca, United States of America,
1 - Inland Waterway System Dynamics
Furkan Oztanriseven, University of Arkansas, 1900 N. Garland
Avenue, Apt. #26, Fayetteville, AR, 72703, United States of
America, , Heather Nachtmann
The growth of population and economic advancement led to a higher demand for
transportation services. However, the expansion in the transportation sector
comes with substantial costs, such as higher gas emissions and traffic congestion.
Inland waterways offer an environmental friendly and economically sound
transportation alternative. In this study, we utilized a system dynamics model to
better understand the interconnected relationships between the economy and the
inland waterways performance.
2 - A Novel Mathematical Model for Water Value Chain Management
Mengqi Hu, University of Illinois at Chicago, Chicao, IL,
United States of America, , Afshin Ghassemi
The amount of water in the world is limited and a large amount of water is
wasted due to ineffective operation for water infrastructure. A decision model
which can efficiently manage the water value chain is urgently needed to reduce
water consumption. In this research, we develop a model for water management
where the water flows at different stages of transitions (e.g., source, distribution)
are modeled. The proposed model is demonstrated to be able to significantly
reduce water consumption.
3 - Automated Analysis of Online Reviews to Improve Visitor
Experience in New York State Parks
Nagesh Gavirneni, Associate Professor, Cornell University, 325
Sage Hall, Ithaca, NY, United States of America,
Hari Udayapuram
The New York State Park system has a large volume of visitor feedback on online
platforms such as Yelp, TripAdvisor, and Google. We design, develop, and
implement software systems that can download, organize, and analyze the text
from these online reviews and help the park managers identify strategies to
improve the visitor experience at their facilities.
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