Informs 2015

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INFORMS Philadelphia – 2015
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SA21
2 - Event-driven Predictive Models for Socio-economic Indicators
Lakshminarayana Subramanian, Associate Professor, NY, NY,
United States of America, ,
Sunandan Chakraborty
I will describe how to extract real-world events using unstructured news streams
to understand their impact on the volatility macro-economic indicators. The
hypothesis is that the factors triggering sudden fluctuations in such indicators can
be characterized by events. Given a news corpus, we describe how to build event-
driven predictive models that can potentially predict fluctuations in specific
indicators. We describe specific results about what triggers fluctuations in food
prices in India.
3 - Efficient Coflow Scheduling in Data Center Networks
Yuan Zhong, Columbia University, 500 W. 120th Street,
New York, NY, 10027, United States of America,
Cliff Stein, Zhen Qiu
In this talk, we consider the efficient scheduling of coflows - an abstraction
introduced in [Chowdhury and Stoica 2012] to capture communication patterns
of large-scale data center jobs. We introduce the problem of minimizing the total
weighted coflow completion times, show that it is strongly NP-hard, and develop
the first polynomial-time approximation algorithms for this problem. We also
evaluate the practical performances of a variety of algorithms through numerical
experiments.
4 - Optimizing for Tail Response Times of Cloud Clusters
Lydia Chen, IBM Zurich, Natarajan Gautam
Motivated by the volatile system dynamics in cloud cluster, we develop an
approximation scheme that can capture the high performance variability caused
by neighboring VMs, especially in terms of tail response times. The approximation
of tail response times is based on the large deviation analysis. We evaluate the
proposed analysis on simulation as well a wiki prototype cluster in the cloud.
SA21
21-Franklin 11, Marriott
Stochastic Models for Medical Decision Making and
Healthcare Delivery
Sponsor: Health Applications
Sponsored Session
Chair: F. Safa Erenay, Assistant Professor, University of Waterloo, 200
University Ave. CPH 4323, Waterloo, Canada,
1 - The Impact of Optimization on the Allocation of Livers for
Organ Transplantation
Mustafa Akan, Associate Professor, Carnegie Mellon University,
5000 Forbes Avenue, Pittsburgh, PA, 15213, United States of
America, James Markmann, Heidi Yeh,
Zachary Leung, Sridhar Tayur
Patients on the waitlist for liver transplantation are prioritized according to their
MELD scores, which reflects the severity of liver disease. Recent studies have
shown that hepatocellular carcinoma (HCC) patients have significantly higher
liver transplant rates than non-HCC patients. We recommend a family of
alternative MELD score policies based on a fluid model approximation of the
queueing system and an optimization model that achieves an optimal balance
between efficiency and equity.
2 - Physician Staffing in the Emergency Department:
Opening the Blackbox
Caglar Caglayan, Georgia Institute of Technology, Atlanta, GA,
United States of America, , Kalyan
Pasupathy, David Nestler, Mustafa Sir, Thomas Hellmich,
Turgay Ayer, Gomathi Marisamy, Thomas Roh
We propose an “intuitive”, “realistic” and “tractable” model of the emergency
department (ED) by a multi-class multi-stage queuing network with multiple
targeted service levels. Based on infinite-server approximation and offered load
analysis, we employ square-root safety principle to determine the right number of
physicians in the ED. Our model is detailed enough to capture the key dynamics
of the ED but simple enough to understand, infer results and implement in a
clinical setting.
3 - Deriving Better Strategies for Influenza Vaccines Allocation
F. Safa Erenay, Assistant Professor, University of Waterloo,
200 University Ave. CPH 4323, Waterloo, Canada,
Osman Ozaltin, Onur Ozden Dalgic
In influenza pandemics, available vaccines are allocated considering individual
risk profiles of the patients. Using a network based stochastic simulation model
and mesh adaptive direct search, we derived effective age-specific vaccine
allocation strategies for cost, health outcomes, and equity metrics. In most
scenarios, the proposed method outperforms the current guidelines and policies
developed based on deterministic compartmental models.
4 - Reliable Facility Location Model for Disaster Response
Abdelhalim Hiassat, PhD Student, University of Waterloo,
, Osman Ozaltin, F. Safa Erenay
We formulate a reliable facility location model for disaster response, and consider
the problem of minimizing expected service cost. Candidate facility locations
might become unavailable after the disaster, and victims patronize relief facilities
based on their preferences. We propose a Lagrangian-decomposition-based
branch-and-bound method for this problem. Our computational results show the
efficiency of the solution approach and the significance of incorporating
preferences into the model.
SA22
22-Franklin 12, Marriott
Matching Markets
Sponsor: Applied Probability
Sponsored Session
Chair: Itai Ashlagi, MIT, 100 Main St, Cambridge, MA, 02139,
United States of America,
1 - Welfare-sensitive Assortment Optimization: An Application to
School Choice
Peng Shi, MIT Operations Research Center, 1 Amherst Street,
E40-149, Cambridge, MA, 02139, United States of America,
In many settings, a planner gives a set of options to agents who choose among
them to maximize their own value, but agents’ choices have externalities on
system revenue/cost. Examples include school choice, public housing, and health
insurance. Welfare-Sensitive Assortment Optimization is to find a set of options
that maximize the sum of agents’ values and system revenue. We give efficient
algorithms under MNL utilities and various constraints, and apply this to improve
school choice in Boston.
2 - Near Feasible Stable Matchings with Couples
Thanh Nguyen, Krannert School of Management, Purdue
University, West Lafayette, IN, United States of America,
, Rakesh Vohra
The National Resident Matching program strives for a stable matching of medical
students to teaching hospitals. With the presence of couples, stable matchings
need not exist. For any student preferences, we show that each instance of a
matching problem has a `nearby’ instance with a stable matching. The nearby
instance is obtained by perturbing the capacities of the hospitals.
3 - Matching with Externalities
Jacob Leshno, Columbia University, 3022 Broadway, Uris Hall,
406, New York, NY, 10027, United States of America,
We show existence of stable matching in markets with a continuum of students.
Stable matchings are characterized as rational expectations market clearing
cutoffs.
4 - What Matters in Tie-breaking Rules? How Competition
Guides Design
Afshin Nikzad, Stanford University, 37 Angell Court, APT 116,
Stanford, Ca, 94305, United States of America,
, Assaf Romm, Itai Ashlagi
School districts that adopt the Deferred Acceptance (DA) mechanism to assign
students to schools face the tradeoff between fairness and efficiency when
selecting how to break ties among equivalent students. We analyze a model with
with random generated preferences for students and compare two mechanisms
differing by their tie-breaking rules: DA with one single lottery (DA-STB) and DA
with a separate lottery for each school (DA-MTB). We identify that the balance
between supply and demand in the market is a prominent factor when selecting a
tie-breaking rule. When there is a surplus of seats, we show that neither random
assignments under these mechanisms stochastically dominates each other, and,
the variance of student’s assignments is larger under DA-STB. However, we show
that there is essentially no tradeoff between fairness and efficiency when there is
a shortage of seats: not only that DA-STB (almost) stochastically dominates DA-
MTB, it also results in a smaller variance in student’s rankings. We further find
that under DA-MTB many pairs of students would benefit from directly
exchanging assignments ex post when there is a shortage of seats, while only few
such pairs exist when there is a surplus of seats. Our findings suggest that it is
more desirable that ``popular” schools use a single lottery over a separate lottery
in order to break ties, while in other schools there is a real tradeoff.