Informs 2015

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INFORMS Philadelphia – 2015
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3 - Solving A Real-world Snow Plow Optimization Problem:
An Integrated Solution Approach
Joris Kinable, Post-doctoral Researcher, Carnegie Mellon
University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, United
States of America, , Stephen F. Smith,
Willem-jan Van Hoeve
Each year, many northern cities are faced with significant expenditures pertaining
winter road maintenance. Snow plowing constitutes a significant part of these
costs. This work presents an integrated, adaptive solution approach for a real-
world snow plow optimization problem. The large number of routing and
scheduling constraints render this problem particularly hard to solve. The
performance of our solution approach is demonstrated on data from the city of
Pittsburgh (USA).
4 - On Solving Quadratic Assignment Problems in
Wireless Communications
Hans Mittelmann, Arizona State University, Box 871804, Tempe,
AZ, United States of America,
In digital wireless communications optimal index assignment leads to difficult
quadratic assignment problems. Those are standard QAPs for single transmissions
or for sequential multiple transmissions. They become higher dimensional QmAPs
when simultaneously optimizing over several retransmissions. We report on the
exact and approximate solution of such problems that arise in practice.
WA21
21-Franklin 11, Marriott
Health Care Operations
Sponsor: Health Applications
Sponsored Session
Chair: Qiushi Chen,
1 - Can an Early Warning Score Predict Patients’ Hospital Length of
Stay and Mortality?
Nasibeh Azadeh-fard, PhD Candidate, Virginia Tech, 544
Whittemore Hall, Virginia Tech, Blacksburg, VA, 24061,
United States of America, , Jaime Camelio,
Navid Ghaffarzadegan
The Modified Early Warning Score (MEWS) is used in hospitals to quickly predict
and prevent catastrophic events such as death. The prediction power of MEWS,
however, is an empirical question. We analyze effectiveness of MEWS in a major
hospital in the US over six months for a sample of 1021 patients. We find that
MEWS modestly predicts hospital length of stay and death, while physicians’
specific characteristics and their subjective assessments are much better predictors
of health outcomes.
2 - Routing Patients to Community Health Services to Maintain
Patient Access after Facility Merger
Aaron Ratcliffe, Assistant Professor, University of North Carolina
at Greensboro, 438 Bryan Building, P.O. Box 26170, Greensboro,
NC, 27402, United States of America,
Merging the facilities dedicated to a health service may allow for cost savings in
terms of economies of scale and other efficiency improvements at the expense of
poorer access to services for patients. We develop a queueing network model to
examine how a social planner should route heterogeneous patient classes to
community health resources to improve patient access in the absence of a
previously dedicated facility.
3 - Integrated Staff and Room Scheduling for Surgeries:
Methodology and Application
Sandeep Rath, PhD Candidate, UCLA Anderson, B501 Gold Hall,
UCLA Anderson, Los Angeles, CA, 90024, United States of
America, , Kumar Rajaram
We consider the problem of minimizing resource usage and overtime costs across
multiple parallel resources such as anesthesiologists and operating rooms at a
large multi-specialty hospital. We develop a two stage optimization program with
recourse. We develop a data driven robust optimization method that solves large-
scale real-sized versions of this model close to optimality. We validate and
implement this model as a decision support system at the UCLA Ronald Reagan
Medical Center.
4 - Arbovirus Risk Maps in Texas
Xi Chen, University of Texas at Austin, Austin, TX 78712,
Austin, TX, United States of America, ,
Nedialko Dimitrov
Dengue fever and Chikungunya virus two key mosquito-borne diseases in Texas.
To focus state resources, public health officials need to identify the geographic risk
areas for these diseases. We consider thousands of possible risk models, based on
maximum entropy methods, combined with data on the transmission vectors,
environmental, and socio-economic factors. We select the best model empirically,
using historical Texas Dengue data. The final model is in use by Texas health
officials.
WA23
23-Franklin 13, Marriott
Stochastic Modeling and Analysis with Applications
Sponsor: Applied Probability
Sponsored Session
Chair: Jing Dong, Northwestern University, 2145 Sheridan Road, Tech
C210, Evanston, United States of America,
Co-Chair: Jose Blanchet, Associate Professor, Columbia University, 500
W 120th St., Mudd Building, IEOR, 3rd Floor., New York, NY, 10027,
United States of America,
1 - Stationarity and Interchange of Limits in Heavy Traffic Analysis
Hengqing Ye, Associate Professor, Hong Kong Polytechnic
University, Hung Hom, Kowloon, Hong Kong - PRC,
David D. Yao
We develop a streamlined approach for justifying the heavy traffic stationary
approximation of stochastic processing networks. First, we demonstrate that the
stability of a deterministic dynamic complementarity problem is sufficient for both
the diffusion limit and pre-limit networks to have stationary distributions. Then,
given an additional mild condition, we show the convergence of stationary
distributions of pre-limit networks to that of the diffusion limit.
2 - Resource Allocation in Bike Sharing using Coupling and
Linear Programming
Shane Henderson, Professor, Cornell University, Rhodes Hall,
Ithaca, NY, 14853, United States of America, ,
David Shmoys, Eoin O’mahony
We propose an optimization problem that allocates bike racks and bikes to
stations across a city. The objective is a transient performance measure from a
continuous-time Markov chain. We show that the objective possesses a (joint)
discrete convexity property that allows for efficient solution via linear
programming. The proof uses a combination of geometrical arguments and
coupling theory. The results are illustrated using Citibike data in NYC.
3 - Tail Analysis Without Tail Information: A Worst-case Perspective
Henry Lam, Assistant Professor, University of Michigan, 1205
Beal Ave., Ann Arbor, MI, 48109, United States of America,
Clementine Mottet
One common bottleneck in tail modeling is that, due to their very nature, tail
data are often very limited. Rather than using conventional parametric fitting, we
will describe a robust alternative that is based on a worst-case analysis under the
geometric premise of tail convexity. We demonstrate that the worst-case convex
tail behavior is either extremely light-tailed or extremely heavy-tailed, and
construct numerical schemes to distinguish between the two cases and find the
worst-case tail.
4 - On the Stability of Matching Queues
Pascal Moyal, Université de Technologie de CompiËgne,
Rue du Dr Schweitzer, Compiègne, 6023, France,
, Ohad Perry
Consider a model in which, to each node of a graph G is associated an arrival
process, and any entering item associated to node k either leaves the system if it
finds in line, another item corresponding to a neighbor of k, or is stored in queue.
Using fluid analysis, we investigate the stability of such matching models, which
are of increasing practical importance. We show that, aside for a specific class of
graphs, they can always be unstable even under a natural necessary stability
condition.
WA21