Minority Issues
Forum Student Poster Presenters
INFORMS 2011,
Charlotte, NC
November 13,
2011
Integer Programming Techniques for Matroid Circuit Problems
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.
Computational Study of Decomposition Algorithms for Mean-Risk
Stochastic Programs
Tanisha G. Cotton
Industrial and Systems Engineering
Texas A&M University
Abstract
To
introduce risk into stochastic programs, convexity preserving dispersion
statistics such as quantile-deviation and absolute semi-deviation can be used
to represent mean-risk objectives. In this poster presentation, we report on a
computational study of decomposition algorithms for stochastic linear programs
using standard instances from the literature.
Coxian 2-Phase Approximation
and Analysis of a Terminating Queueing System: A Border Crossing Model
Hiram Moya
Industrial and Systems Engineering
Texas A&M University
Abstract
The U.S. international land boundary is a
volatile, security intense area. In 2009, the combined trade was $735
billion within NAFTA, with 80% transported by commercial trucks through “ports
of entry (POE)”. Increasing security and inspection requirements are seriously
affecting transit times. Each POE is configured as a multi-commodity,
prioritized queueing network which rarely, if ever, operates in steady state.
This paper provides a summary of transient queueing network analysis conducted
to analyze throughput rates, queue lengths, cycle times and configuration
effectiveness. Particular emphasis is given to the dynamic reallocation
of inspection (service) facilities and inspectors under time-varying arrivals
(demands).
Fluid Model
of the Dynamics of Patients and Physicians in Emergency Rooms
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
treatment. We propose a fluid model using first order ordinary differential
equations to approximate the dynamics of patients and physicians in emergency
rooms. We then apply classical control theory mechanics to determine the
optimal control function to minimize patients’ holding costs and physicians’
utilization costs. Numerical solutions of our fluid model suggest a continuous
control function, which we discretize, using least square and mean value
methods, to approximate the best shifts staffing policy of physicians.
Skewness
Variance Approximation for Dynamic Rate Multi-Server Queues with Abandonment
Jamol J. Pender
Industrial and
Financial Engineering
Princeton
University
Abstract
A fundamental dynamic rate queueing model for
large scale service systems is a multi-server queue with non-homogeneous
Poisson arrivals and customer abandonment. By scaling the arrival rates and number
of servers of such systems, using the fluid and diffusion limit theorems found
in Mandelbaum, Massey, and Reiman (1998) we can approximate the stochastic
behavior of this queueing process by one that is Gaussian. Moreover, the
approximations to the mean and variance produced by these limiting processes
form a two-dimensional dynamical system. Recent work by Gautam and Ko (2011)
found a modified version of these differential equations and obtained better
estimates of the mean and variance for the original queueing system. We now
introduce a new three-dimensional dynamical system that surpasses these two approaches.
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Planning
Transportation of Disaster Relief Goods with Rural Recipients
Luis de la
Torre
Industrial
Engineering and Management Sciences
Northwestern
University
Abstract
Disaster
relief presents many unique logistics challenges, with problems including
damaged transportation infrastructure, limited communication, and coordination
of multiple agents. We present ongoing work on the problem of distributing
goods after a disaster to many rural beneficiaries throughout the
disaster-affected region. We model the problem with two-stage stochastic
programming with uncertainty in the accessibility of beneficiaries and
long-haul transportation to beneficiaries planned before accessibility is
known. We test our model using data from simulated disaster settings, including
earthquakes in the New Madrid fault zone. We use sample average approximation
and local search heuristics to find high-quality solutions that consider
fairness and equity to recipients along with efficiency. This is joint work
with Irina Dolinskaya and Karen Smilowitz.
Emergency
Medical Service Allocation in Response to Large Scale Events
Gabriel Zayas-Caban
School of Operations Research and Information
Engineering
Cornell University
Abstract
In the event of a catastrophic or large scale event
demand for Emergency Medical Service (EMS) vehicles in the affected region will
almost certainly overwhelm the available supply. In such cases, it is necessary
for cities in the affected region to request aid (in the form of added
capacity) from neighboring municipalities in order to bring the affected region
back to its day-to-day levels of operation. In this paper, we propose a
systematic method to address such scenarios.
In
particular, we consider a region consisting of several cities, where each city
is in charge of managing its own set of EMS vehicles. We propose that a
centralized or statewide decision-maker coordinate the temporary transfer of
resources (EMS vehicles) from cities in the unaffected region into the cities
in the affected region. We model
the control of each city's EMS vehicles as a multi-server queueing system and
use classical results to estimate the number of vehicles available at each
city. We then develop a knapsack
model to guide the allocation of available vehicles from the donor area into
the affected one and a clearing system model to dynamically control the added
resources.
As
the dimension of the problem is large, a heuristic we call the buddy system is
proposed where cities are paired to form city groups. This reduces the size of
the problem enough to solve the knapsack problem for initially allocating
vehicles to city groups. Within the city groups the clearing system model is
solved via Markov decision processes. The performance of our heuristic is
compared to several other reasonable heuristics via a detailed numerical study.
Results show that the buddy system exhibits significant cost and time savings,
and is generally robust to varying parameters.