Minority Issues
Forum Student Poster Presenters
INFORMS 2009,
San Diego, CA
November 7,
2010
Integer Programming Techniques for Matroid
Circuit Problems
John Arellano
Computational
and Applied Mathematics
Rice University
Abstract
Although
some combinatorial optimization problems associated with matroids
can be solved in polynomial time, finding particular circuits in matroids is an NP-hard problem. It is related to
compressive sensing and finding the degree of redundancy of sensor networks. We
attempt to solve these types of problems to optimality using integer
programming techniques and present computational results.
A Computational Study of Decomposition Algorithms for
Stochastic Programs with Mean-Risk Objectives
Tanisha G.
Cotton
Industrial and Systems Engineering
Texas A&M University
Abstract
To
introduce risk into linear stochastic programs, convexity preserving dispersion
statistics, quantile- deviation and absolute semideviation can be used to represent mean-risk
objectives. In this poster presentation, we report on a computational study of
stage-wise decomposition algorithms for this class of stochastic programs.
Fast
Generalized Subset Scan for Anomalous Pattern Detection
Edward McFowland III
Heinz School of
Public Policy and Management
Carnegie-Mellon
University
Abstract
We propose Fast Generalized Subset Scan (FGSS), a new method for
detecting anomalous patterns in datasets with categorical variables. We frame the pattern detection problem
as a search over subsets of data records and attributes, and exploit a novel
property of the nonparametric scan statistic that allows for efficient
optimization over subsets without an exhaustive search over the exponentially
many subsets. As a result of this
efficient optimization, we can quickly find the subset of records that is
optimal for a given set of attributes; similarly, we can efficiently optimize
over all subsets of attributes for a given subset of records. The algorithm iterates between
maximizing over records and attributes until it converges to a local maximum,
which represents a group of anomalous records and the set of attributes for which
they are anomalous. Choosing the maximum of multiple randomly restarted
searches discovers the global maximum with high probability.
Our results demonstrate that
FGSS can successfully detect useful anomalous patterns in various application
domains, including disease surveillance, customs monitoring, and network
intrusion detection. FGSS dramatically reduced run-times and achieved higher
detection power than current methods on massive multivariate dataset.
Risk-Based
Technology Assessment for Capital Equipment Decisions in Small Firms
Samuel Merriweather
Industrial and
Systems Engineering
Texas A&M
University
Abstract
Within
this presentation we discuss a risk-based approach to capital equipment
budgeting and acquisition of new equipment and/or technologies in small firms.
The approach overcomes deficiencies in the capital budgeting process by
connecting equipment use to projected cash flows via discrete-event simulation.
A healthcare application is illustrated.
Transient Analysis of the Border Crossing
Process Using Congestion Based Policies
Hiram Moya
Industrial and Systems Engineering
Texas A&M University
Abstract
Trade is the U.S. depends on an efficient flow
of inspected containers in and out of the border ports of entry (POE), while
focusing on security, and being cost effective. This research focuses on all
commercial traffic at a southern border POE, where there is a non-steady state,
terminating system. Using transient analysis, we present analytical and
experimental results of congestion based policies with a fixed number of
servers, by implementing a primary inspection station service switch.
Differential Equations Modeling
of Patients and Physicians Dynamics in Emergency Rooms: Issues and Optimal
Control Staffing Policies
Jerome Ndayishimiye
Industrial and Systems Engineering
University of Buffalo
Abstract
Hospital emergency rooms are difficult to manage
because of the complexity of allocating costly resources, mainly physicians, in
the light of the dynamical arrivals of patients and the costs of delayed
medical care. We propose using ordinary differential equations to model the
dynamics of patients and physicians and then use optimal control theory to
determine optimal physicians allocation policy. For a practical implementation
of the policy, we use a heuristic algorithm based on the concepts of least
squares and mean value of the optimal control function to determine the number
of shifts and the number of physicians needed.
Risk Management
for Call Center Staffing
Jamol J. Pender
Industrial and
Financial Engineering
Princeton University
Abstract
In this work, we intend to explore the
control of dynamic rate queueing systems. Optimal
control of dynamic rate queueing
systems have many applications in telecommunication systems as well as
call centers. In call center staffing it is of utmost importance to staff
the call center with the minimum number of agents to achieve a level of
satisfaction that is acceptable for the manager. In this report we
primarily look at call center from a manager and shareholder perspective. From
the manager perspective, we want to staff the call center with the minimum
number of agents, however, from a shareholder we want to make a consistent
profit from investing in our call center. These two objectives are
explored and considered in this work.
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Characterizing the Impact of
Risk Factors on Mortality for Breast Cancer Patients
Shengfan Zhang
Edward P. Fitts Department of Industrial and Systems Engineering
North Carolina State
University
We
model and compare mortality for breast cancer patients using community-based
Carolina Mammography Registry data. Cumulative incidence function is used to
estimate mortality probabilities from breast cancer and other causes as a
function of patient age, race, cancer stage at diagnosis and breast
cancer risk factors (breast density, estrogen and progesterone receptor
status, and family history of breast cancer). Methods for approximating
confidence intervals are also applied to enable the comparison among different
risk groups. Left censoring is incorporated using a multifaceted cancer growth
model to quantify the lag between actual start time of breast cancer and
diagnosis time (recorded cancer start date).