2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
301
Multi-agent Routing In Shared Guide Path Networks
Greyson Daugherty, Georgia Institute of Technology, 765 Ferst Dr
NW, Atlanta, GA, 30318, United States,
Spyros Reveliotis, Greg Mohler
This poster describes a heuristic algorithm for minimizing the makespan required
to route a set of agents inhabiting a shared guidepath network from their initial
locations to their respective destinations. The work is motivated by operations
taking place in the context of some unit-load material handling systems like zone-
controlled AGV systems, as well as in quantum computers. This poster presents a
brief description of the considered problem and of the inner workings of the
proposed algorithm, along with a set of computational results that reveal the
efficacy of the derived solutions and directions for further research.
Cost Minimization Of Government Issued Cell Phones
John Yannotty, Slippery Rock University, 1 Morrow Way,
Slippery Rock, PA, 16057, United States,
Alexander Reid Barclay
Increasing costs associated with DSL circuits has led the United States Pentagon to
study consolidation of its wireless network in attempt to minimize annual
expense while maximizing efficiency. During consolidation, Private Virtual
Channels (PVCs) are transferred from either low utilized or expensive circuits to
existing circuits with higher utilization and lower annual cost. The consolidation
process is further constrained by region, service package (VCI code), and
utilization capacity per circuit. Through the use of an optimized network annual
expenses are decreased from approximately $11 million to $3.1 million.
Visualization Of Cross Validation For Prediction And
Classification
Alexander Engau, Associate Professor, University of Colorado
Denver, P.O. Box 173364, Department of Mathematical Sciences,
Denver, CO, 80217-3364, United States,
Paola Andrea Gonzalez
In supervised learning, the performance of data mining and machine learning
algorithms is often measured and compared only numerically using cross
validation. Here, we describe two case studies in which we can complement and
further extend such numerical comparisons with a visualization in the form of
new validation maps. These validation maps are illustrated using several state-of-
the-art classifiers from Scikit-Learn and offer substantial new insights into
advantages and remaining limitations of support vector machines, decision trees,
boosting and discriminant analysis.
Bayesian Optimization Of Predictive Precision For Business
Ledger Analytics
Abhinav Maurya, Carnegie Mellon University, 5634 Stanton
Avenue, Apt 306, Pittsburgh, PA, 15206, United States,
, Aly Megahed
Predicting changes in account revenues is of vital importance to a business in
order to take action on accounts that are predicted to shrink, and to learn from
success stories of offerings that led to maximum revenue growth. However, the
corresponding datasets are often imbalanced, and therefore accuracy is a poor
metric to optimize for. We present a Gaussian Process-based method that
maximizes precision, yielding actionable results without sacrificing much
accuracy. We find that our method gives better results than exhaustive uniform
grid search, since Gaussian Process-based optimization can focus on areas of
parameter space that have higher chances of attaining the maximum objective
value.
The Continuous Network Location Problem For The Alternative
Fuel Refueling Station
Sang Jin Kweon, PhD Candidate, Pennsylvania State University,
310 Leonhard Building, Industrial and Manufacturing
Engineering, University Park, PA, 16802, United States,
Unstable price of oil and concerns about the finite nature of reserves, coupled
with increasing awareness of the environmental issues caused by the burning of
fossil fuels, increased spotlight on alternative-fuel vehicles. In order to stimulate
the use of alternative-fuel vehicles, the inherent problem with the lack of
refueling infrastructure must be resolved. In this study, we propose a novel
methodology to locate an alternative-fuel refueling station on a road network
with the objective of maximizing the total traffic flow covered by the station, so
that more customers are able to refuel their alternative-fuel vehicles.
Optimal Number Of Choices In Rating Contexts
Sam Ganzfried, Assistant Professor, Florida International
University, 11200 SW 8th St, Miami, FL, 33199, United States,
In many settings people give numerical scores to entities from a small discrete set,
e.g., attractiveness from 1-5 on dating sites and papers from 1-10 for conferences.
We study the problem of understanding when using a different number of options
is optimal. We study several natural processes for score generation. One may
expect that using more options always improves performance, but we show that
this is not the case, and that using fewer choices—even just two—can surprisingly
be optimal. Our results suggest that using fewer options than typical could be
optimal in certain situations. This would have many potential applications, as
settings requiring entities to be ranked by humans are ubiquitous.
Improving Patient Access To Primary Care Through E-visits
Xiang Zhong, University of Florida, Gainseville, FL, 32601,
United States, Jingshan Li, Philip Bain,
Albert Musa, Peter Hoonakker
To improve primary care access, many healthcare organizations have introduced
electronic visits to provide patient-physician communication through securing
messages. In this study, we introduce an analytical model to characterize primary
care physician’s operations on office and e-visits, and other non-direct care tasks.
Analytical formulas to evaluate the mean and variance of patient office visit and
e-visit cycle times are derived, and discussions of the impacts of e-visits on
traditional primary care delivery are carried out. It can be observed that the
patient e-visit to office visit referral ratio plays an important role in determining
whether it’s beneficial to conduct e-visits.
Optimizing Inventory Under Non-stationary Demand For
Profitability Improvement
Liu Yang, Assistant Professor, Purdue University, 3000 Technology
Ave, New Albany, IN, 47150, United States,
This research presents a multi-period optimization model that integrates
inventory classification and control decisions to maximize the NPV of profit. The
model explicitly addresses nonstationary demand, limited inventory budget,
arbitrary reviews periods, and SKU-specific lead times and holding costs. The
model is applied to a real-life company that currently uses the multi-criteria
inventory classification, and improves its profit by nearly 3%. The comparison to
the ABC shows an average profit increase of 7.5%. We find that profit is
insensitive to the number of classes in a wide range, but when the budget is tight,
a large number of classes with a wide range of service levels is optimal.
Analytics In Action: When Will I Get Out Of The Hospital?
Modeling Length Of Stay Using A Disease Network
Pankush Kalgotra, PhD Candidate, Oklahoma State University,
308 W Maple Avenue, Apt 5, Stillwater, OK, 74074, United States,
, Ramesh Sharda
Comorbidity is a medical condition when a patient develops multiple diseases
simultaneously. We examine the impact of comorbidity on the patient’s hospital
length of stay (LOS). We present a unique approach to model comorbidities by
creating a network of diseases from the pair-wise combinations of the diseases
diagnosed in the 1.6 million patient visits in US hospitals in 2011. Using the
small-world property of the network, we proposed a new comorbidity score for a
patient. Finally, we built an explanatory and predictive model on the patient visits
in 2012, to predict a patient’s LOS. The model with our proposed comorbidity
score outperformed the existing models.
The GetFruved Project Uses Integer Programming To Match
Freshmen To Peer Mentors
Wangcheng Yan, The University of Tennessee, Knoxville,
Knoxville, TN, 37996, United States,
Wenjun Zhou, Sarah Colby, Kendra Kattelmann, Anne Mathews,
Melissa D. Olfert
In this study, we took a quantitative approach to the friend-matching problem to
assign peer mentors (PMs) to freshmen (FMs) recruited for the GetFruved project.
A 20 “fun”-question survey was used to develop the PM-FM matching algorithm.
Data were collected from two semesters. Semester 1 served as training period, and
matching was made in Semester 2. Our strategy was to train a model with logistic
regression, and optimize the matching with integer programming. The empirical
study verified the homophily theory, and demonstrated the effectiveness of our
approach to identifying the optimal PM assignments.
Dynamic Model Validation Metric Based On Wavelet
Thresholded Signals
Andrew D Atkinson, Captain, Air Force Institute of Technology,
2950 Hobson Way, Wright-Patterson AFB, OH, 45433,
United States, , Raymond R Hill
Model validation is a vital step in simulation development to ensure that a model
is sufficiently representative of the system. Transient phase model validation
deserves special attention because the experimental system data is often
contaminated with noise, due to the short duration and sharp variations in
transient data. We propose a process to validate the transient phase of a model
that uses wavelet thresholding to de-noise the data signals and calculates a
validation metric that incorporates shape, phase, and magnitude error. A
simulation study and empirical data from an automobile crash study illustrate the
wavelet thresholding validation approach.
Joint Service In Primary Care Clinics
Hyo Kyung Lee, University of Wisconsin-Madison, 313 N Frances
St Apt 601, Apt 601, Madison, WI, 53703, United States,
Xiang Zhong, Jingshan Li, Albert Musa,
Philip Bain
To improve patient flow and reduce provider workload, joint service has been
proposed and implemented in many primary care clinics. As no model is available
yet to quantify joint visit’s impact, we introduce Markov chain models of patient
flow with joint visits, and investigate the system behavior under different
scenarios. Particularly, to reduce the state space dimension, convergent iterative
procedures are proposed. Furthermore, the study is extended to non-Markovian
case by introducing empirical formulas. To illustrate the applicability of the
methods, an application study at Dean East Clinic is presented.
POSTER SESSION