Minority Issues
Forum Student Poster Presenters
INFORMS 2012, Pheonix, AZ
October 14,
2012
Special
Acknowledgement to the National Science Foundation (CMMI-1130507)
Cocircuits of Linear Matroids
John Arellano
Computational
and Applied Mathematics
Rice University
Abstract
I present a set covering problem (SCP) formulation of the matroid cogirth problem.
Addressing the matroid cogirth
problem can lead to significantly enhancing the design process of sensor
networks. The solution to the matroid cogirth problem provides the degree of redundancy of the
corresponding sensor network, and allows for the evaluation of the quality of
the network. I provide computational results to validate a branch-and-cut
algorithm that addresses the SCP formulation.
A Simulation-Optimization Approach for Large-Scale Wildfire
Response Planning
Michelle
Alvarado
Industrial and
Systems Engineering
Texas A&M
University
Abstract
Extended
wildfire response planning under uncertain weather conditions is challenging.
We introduce a new simulation-optimization approach that integrates a fire
behavior and suppression simulation model (DEVS-FIRE) and a stochastic integer programming model (SIP). Using weather forecasts,
DEVS-FIRE makes predictions of fire growth and the effects of suppression
efforts on the fire, and feeds this scenario data to SIP to optimize the
dispatching of firefighting resources to the fire over time.
An Approach to Approximating Contributions Received through
Food Bank Collections at Grocery Stores
Luther G. Brock
III
Industrial and
Systems Engineering
North Carolina
A&T State University
Abstract
This research addresses the need to approximate how much
food is received by food banks through collections at grocery
store. Such forecasts are the basis for more cost-effective vehicle
scheduling. A feed-forward artificial neural network (FF-ANN) is used to
estimate the amounts of different in-kind goods received through isolated store
collections. The FF-ANN is trained using donation records provided by a food
bank in the southeastern United States and compared with linear regression.
A Decision Support Framework for Healthcare Transition
Programs to Reduce Hospital Readmissions
Sabrina Casucci
Industrial and
Systems Engineering
University of
Buffalo
Abstract
High hospital readmissions signal poor quality of
post-hospital care and unnecessarily increase healthcare costs. Intervention
programs aim to reduce readmission rates; yet there is little support for
understanding the effect of physician and patient care decisions on the program
outcomes. This paper focuses on the complexities of transitional care and the
design of informative stochastic models that can provide decision support to
all the stakeholders.
A Joint Surveillance and Patrol Problem for Law Enforcement
Belleh Fontem
Information
Systems, Statistics, and Management Science
University of
Alabama
Abstract
We investigate the problem of using unmanned aerial
systems (UASs) as visual aids to ground agents while
assigning them dangerous incidents in order to maximize the cumulative harm
averted to society. We introduce a tight formulation of the resulting team
orienteering problem and draw insights from theoretical analyses and numerical
experiments.
Integrating Univariate Control
Charts and Mahalanobis Distance for Near Zero Type II
Error Monitoring
Weihong Guo
Industrial and
Operations Engineering
University of
Michigan
Abstract
It is highly needed to ensure high product
quality through online sensing monitoring for emerging manufacturing processes
especially in producing mission- critical parts such as rechargeable batteries
for electrical vehicles. This research proposed an integrated monitoring scheme
that targets at a near zero Type II error rate by integrating univariate control charts and Mahalanobis
distance. The detailed algorithms and its effective and implementation in a
real-world example will be presented.
Two-Stage Stochastic Programming Model for Phlebotomist
Scheduling in Hospital Laboratories
Laquanda Leaven
Industrial and
Systems Engineering
North Carolina
A&T State University
Abstract
Laboratory
medicine is vital to the healthcare system. A Two-Stage Stochastic Programming
Model has been formulated to determine better phlebotomist schedules for the preanalytical stage of the testing process in hospital
laboratories. The objective is to balance the workload among shifts and the
workload among phlebotomists in each shift, which result in cost reductions.
Evaluation of Breast Cancer Mammography Screening Policies
Considering Adherence Behavior
Mahboubeh Madadi
Industrial
Engineering
University of
Arkansas
Abstract
The efficacy of mammography screening guidelines
is highly associated with womenÕs compliance with these recommendations.
However, none of the existing policies take womenÕs adherence behavior into
consideration. Instead, perfect adherence is often assumed. In this study, a
partially observable Markov model is proposed to evaluate and compare various
screening policies, while incorporating variation in womenÕs adherence behavior.
Time Varying Queues with Abandonment: A Laguerre
Polynomial Approach
Jamol Pender
Operations
Research and Financial Engineering
Princeton
University
Abstract
A time varying multi-server queueing
model with abandonment is the ubiquitous choice for modeling service systems.
As with many non-stationary models, we must settle for only understanding their
moment behavior since other information is either very difficult to acquire or
intractable. One way to analyze the moment behavior is through asymptotic
methods. However, in this paper, we take a novel approach that uses the Kolmogorov forward equations. To analyze the moments of our
queueing system, we expand the queueing
process via Laguerre polynomials and use a truncated
expansion to derive approximate expectations and covariance terms. The Laguerre polynomials are useful because they serve as a
basis for the Hilbert space L2[[0, °), ν], where ν is the exponential
measure. This expansion is in contrast to the expansion of Hermite
polynomials used by Massey and Pender, which was
motivated by the Gaussian fluid and diffusion limits. Since the Laguerre are orthogonal with respect to the exponential
measure, our approximation might be more useful for the single server setting
where the Hermite expansion breaks down and the
stationary distribution is exponentially distributed. We show that three terms
of the polynomial series is enough to capture the most meaningful cumulant moments of the queueing
process such as the mean, variance, and skewness.
Vaccine Prioritization Using Operations Research
Tim Schmoke
Industrial and
Systems Engineering
Rochester
Institute of Technology
Abstract
A more
effective vaccine prioritization process is essential to prevent the many
issues that currently weaken the global vaccine supply chain. This research
project aims to create a decision-support tool for prioritizing vaccine
initiatives using mathematical optimization models. The result will be a tool
that researchers and funding agencies can use to determine which initiatives
are more effective and meet the needs of multiple target populations
A Model for Healthcare Supply Chain Coordination and
Treatment Protocol Design Solutions
Lakausha Simpson
Industrial and
Systems Engineering
North Carolina
A&T State University
Analysis of Food Import Refusals as a Key Indicator of Risk
for Food Safety
Jonathan W. Welburn
Industrial and
Systems Engineering
University of
Wisconsin
Abstract
There is rising concern for risks associated with
imported foods. We use import violations data from
the FDA Operational and Administrative System for Import Support (OASIS) to
address these concerns, quantify risks by product and country of origin, and
explore the usefulness of OASIS data.
.
A Game Theoretic Model of Financial Contagion and Global
Economic Risks
Jonathan W. Welburn
Industrial and
Systems Engineering
University of
Wisconsin
Abstract
Global financial crises have revealed
the systemic risk posed by economic contagion. We formulate a game between
countries, central banks, banks, customers, and financial inter-governmental
organizations to model the dynamics of borrowers and lenders. We model
strategic choices, determine equilibrium solutions, and simulate the impacts of
random shocks. Our conclusions
enhance the understanding of global economic risk.
The Minimal k-core Problem for Modeling
k-assemblies
Cynthia Woods
Computational
& Applied Mathematics
Rice University
Abstract
I present a recursive algorithm to find all minimal
k-cores of a given undirected graph. This method is a modification of the Bron and Kerbosch algorithm for
finding all cliques. The presented problem has applications in the area of
neuroscience. For example, in the study of associative memory, a cell assembly
is a group of neurons that are strongly connected and represent a ÒconceptÓ of
our knowledge. A k- assembly is a particular type of cell assembly. It is
defined mathematically as the closure of a minimal k-core. The proposed method
puts us a step closer to determining whether there exists a realistic number of
k-assemblies in a given network. I provide computational and theoretical
results to validate the proposed algorithm.
Optimal Control of an Emergency Room Triage and Treatment
Process
Gabriel Zayas-Caban
Center for
Applied Mathematics
Cornell University
Abstract
Patient care oftentimes consists of assessment and
treatment. These two phases are sometimes carried out by the same
medical provider, so there is a question of how to prioritize the work
in order to balance initial delays for care with the need to discharge patients
in a timely fashion. We model a hospital emergency room triage and treatment
process as a tandem queue with a single server, explore alternative service
disciplines under various scenarios, and identify optimal policies for each.
A Decision Support Framework for Healthcare Transition
Programs to Reduce Hospital Readmissions
Yuanhui Zhang
Edward P. Fitts Department of Industrial and Systems Engineering
North Carolina
State University
Abstract
The main
objective for care of type 2 diabetes is to control the patientÕs glycated hemoglobin (HbA1c) to reduce the risk of the
diabetes complications. Uncertainty in the progression of HbA1c and the
treatment effects make treatment decisions challenging. We present a Markov
decision process to maximize the patientÕs expected quality-adjusted life years
prior to major complications. We present the structure and influence factors of
the optimal policy and compare it to current guidelines.