Minority Issues
Forum Student Poster Presenters
INFORMS 2009,
San Diego, CA
October 11,
2009
Stochastic
Programming Models for Wildfire Initial Response Planning
Julian
A. Gallego
Industrial
and Systems Engineering
Texas
A&M University
Abstract
We present two stochastic
programming models for initial attack: standard response model (SRM) and
explicit fire growth response model (ERM). The SRM assumes a known standard
response needed to contain a fire of given size, while the EFGRM considers fire
behavior characterized as fire perimeter and burned area at discrete time
intervals over the initial response period. We discuss solution methods for the
models and report on a computational study using instances based on
actual data for one of the Texas Forest Service fire planning
unit in East Texas.
Computationally
Tractable Stochastic Integer Programming Models for Air Traffic Flow Management
Charles
N. Glover
Applied
Mathematics and Scientific Computation Program
Institute
of Systems Research
University
of Maryland
Abstract
A primary
objective of Air Traffic Flow Management (ATFM) is to ensure the overall flow
of aircraft through airspace, while minimizing the impact of delays and
congestion on airspace users. Much
of this delay and congestion is caused by the vulnerability fo the airspace to changes in the weather, which can
lower the capacities of different regions of airspace. Combine this uncertainty with the size
of the airspace system and we arrive at a very complex system. This makes the development of efficient
algorithms to solve ATFM problems an important and active area of
research. In this prospectus, I
will introduce some techniques of mathematical programming that can be used to
solve ATFM problems and discuss some algorithms and mathematical models along
with the research they inspire in this area.
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Current Success Factors for Sustaining Kaizen Event Outcomes
Wiljeana J.
Glover
Industrial and Systems Engineering Department
Virginia Polytechnic Institute and State
University
Abstract
A Kaizen event
is a focused and structured improvement project, using a dedicated
cross-functional team to improve a targeted work area, with specific goals, in
an accelerated timeframe. Kaizen
events have been widely reported to achieve successful results upon the
conclusion of the event, however, a major obstacle for many organizations is
the to sustain or improve upon the initial Kaizen event results. This research surveys the Kaizen event,
work area, and post-event factors of approximately 65 Kaizen event teams across
eight organizations. Multivariate
data analysis methods are used to identify the factors that may influence the
sustainability of Kaizen event outcomes.
Reliability
Modeling and Technology Assessment for Capital Equipment Acquisition Decisions
Samuel
Merriweather
Industrial
and Systems Engineering
Texas
A&M University
Abstract
We will discuss a risk-based
approach to capital equipment budgeting and acquisition supported by
reliability/availability life cycle models. We are particularly concerned with technology assessment
decisions where capital equipment budgets carry profound financial risk (e.g.,
small health care facilities) and candidate acquisitions are new technologies
having little operational history.
Assortment Selection in Dual
Sales Channels
Betzabe
Rodriquez
Industrial
and Operations Engineering
University
of Michigan
Abstract
We
consider a build-to-order manufacturer who sells an assortment of products
through both a direct channel and an independent retailer (e.g. Dell selling
through BestBuy). We study the tension between the retailer’s and the manufacturer’s preferences regarding the retailer’s
assortment. We find that the
retailer may wish to carry a smaller assortment in an effort to curb the
manufacturer’s wholesale price.
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Iterative
Methods for Computational Models of Acoustic Scattering
Josef
Sifuentes
Computational
and Applied Mathematics
Rice
University
Abstract
Models
of acoustic scattering through inhomogeneous material is a vital component to
many industrial applications, including seismic imaging in the petroleum
industry, sonar and radar research in the defense industry, medical imaging and
shape optimization of acoustic materials.
One method of creating such models require
computational techniques for computing high order approximations to
Lippmann-Schwinger integral equations.
Discretizations of the Lippmann-Schwinger
integral equation result in large, dense, non-Hermitian
matrix equations. We develop
efficient, Krylov-based iterative methods for solving
these equations based on analysis of the underlying integral operator and its
spectrum.