Informs 2015

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INFORMS Philadelphia – 2015
238
MD19
19-Franklin 9, Marriott
Application of Nonlinear Optimization using
Sequential Linear Programming Techniques
with Xpress
Sponsor: Computing Society
Sponsored Session
Chair: Zsolt Csizmadia, Principal Engineer, FICO, FICO House,
Starley Way, Birmingham, B37 7GN, United Kingdom,
1 - State of Optimization in Advanced Process Control
Rishi Amrit, Shell International, Houston, TX,
United States of America,
Process Control forms the backbone as well as the driving agent for almost all of
process industries today. Smart algorithms combined with superior computational
capabilities allow us to automate processes in a controlled fashion while
optimizing environmental, safety and economic performance. This talk discusses
recent advances in commercial Advanced Process Control technology by
harnessing the latest developments in the optimization community along with the
challenges going forward.
2 - Modeling Recursive Formulae in Xpress using
Variable Eliminations
Libin Varghese, Lead Modeling Developer, FICO, 1500 Broadway,
Suite 1101, New York, NY, 10036, United States of America,
Modeling a deposit pricing problem, that optimizes rates for a multiyear period,
involves handling of various recursive formulae that link each time period to the
next. We shall focus on how we modeled the problem in the Mosel modeling
language using the new variable elimination feature of Xpress-Nonlinear and the
performance improvements achieved.
3 - A New Optimality Measure for Sequential Linear
Programming Methods
Zsolt Csizmadia, Principal Engineer, FICO, FICO House,
Starley Way, Birmingham, B37 7GN, United Kingdom,
The KKT conditions are regarded as the definite first order optimality conditions
for nonlinear programming though regularity conditions relatively rarely hold in
practice. The convergence of nonlinear optimization algorithms based on first
order approximations often focus on the progress made rather than the solution
properties. We introduce a new optimality measure derived from the KKT
conditions and explore the connection between the convergence of first order
methods and the new measure.
MD20
20-Franklin 10, Marriott
Stochastic Models and Analysis for Cloud Computing
Cluster: Cloud Computing
Invited Session
Chair: Yingdong Lu, IBM Research, 1101 Kitchawan Rd, Yorktown
Heights, United States of America,
1 - Model Based Autoscaling of Hadoop Clusters
Parijat Dube, IBM, 1101 Kitchawan Road, Yorktown Heights,
United States of America, , Li Zhang,
Andrzej Kocut, Anshul Gandhi
We develop novel performance models for Hadoop workloads that relate job
execution time to various workload and system parameters such as input size and
resource allocation. We employ statistical techniques to tune the models for
specific workloads, including TeraSort and Kmeans. The tuned models are used to
determine the resources required to successfully complete the Hadoop jobs as per
the user-specified execution time SLA.
2 - Navigating the Amazon Cloud
Aaron Yan, Data Scientist, Gravitant, 11940 Jollyville Road,
#325N, Austin, TX, 78759, United States of America,
Ilyas Iyoob
Selecting the right level of reservation in the cloud is a tricky problem, especially
when there are multiple reservation levels. In this paper, we explore the optimal
levels of reservation for a portfolio of cloud servers that satisfy the CapEx and
OpEx budget. The team has developed a web application that solves this problem
and demonstrates the savings incurred from choosing the correct reservation
pricing models.
3 - Optimal Resource Allocation Algorithms for Cloud Computing
Siva Theja Maguluri, Postdoctoral Researcher, IBM TJ Watson
Research Center, 1101 Kitchawan Road, Yorktown Heights, NY,
10598, United States of America,
Jobs arrive at a cloud computing system according to a stochastic process and
request resources like CPU, memory, etc and need service for a random amount
of time. These jobs need to be scheduled on servers. The jobs are first routed to
one of the servers when they arrive and are queued at the servers. Each server
then chooses a set of jobs from its queues so that it has enough resources to serve
all of them simultaneously. We present an optimal load balancing and scheduling
algorithm.
MD21
21-Franklin 11, Marriott
Stochastic Models in Healthcare
Sponsor: Health Applications
Sponsored Session
Chair: Sait Tunc, UW-Madison, 3233 Mechanical Engineering Building,
1513 University Avenue, Madison, WI, 53706, United States of
America,
Co-Chair: Oguzhan Alagoz, UW-Madison, 3242 Mechanical
Engineering Building, 1513 University Aveneue, Madison, WI, 53706,
United States of America,
1 - Robustness of Markov Decision Processes for Medical
Treatment Decisions
Lauren Steimle, University of Michigan, 1205 Beal Avenue,
Ann Arbor, MI, 48109-2117, United States of America,
Brian Denton
Markov decision process (MDP) models are frequently used to study optimal
policies for treatment of patients with chronic diseases. However, these models
can be sensitive to estimates of transition probabilities and rewards. We discuss an
approach for quantifying robustness of MDP-based policies with respect to
parameter uncertainty. We illustrate our findings based on a model for optimal
treatment of blood pressure and cholesterol in patients with type 2 diabetes.
2 - Ambulance Emergency Response Optimization in
Dhaka, Bangladesh
Justin Boutilier, University of Toronto, 5 King’s College Road,
Toronto, ON, M5S 3G8, Canada, ,
Moinul Hossain, Timothy Chan
Dhaka, the capital city of Bangladesh and the tenth largest city in the world, does
not currently have a centralized emergency medical service (EMS) system or 9-1-
1 type number. As a result, patients experience restricted access to healthcare. To
address this problem, we have developed a novel data-driven robust location-
routing model that can be applied to Dhaka and other developing urban centers.
The model uses traffic data collected via GPS to construct an uncertainty set for
travel times.
3 - Score Based Anticipative Transfer Requests in the
Intensive Care Units
Yasin Ulukus, University of Pittsburgh, Pittsburgh PA,
United States of America, Gilles Clermont,
Guodong Pang, Andrew J. Schaefer
The efficient operation and management of ICUs is critical to providing high
quality of care while managing costs. We construct a new Transfer Score to
estimate readmission and death probabilities. We further show that an
anticipative transfer request policy combined with effective use of clinical markers
can significantly decrease transfer delays without increasing the capacity. We
present a Markov Decision Process (MDP) model for the transfer request problem
and solve it via approximations
4 - Optimal Breast Cancer Diagnostic Decisions under the
Consideration of Overdiagnosis
Sait Tunc, UW-Madison, 3233 Mechanical Engineering Building,
1513 University Avenue, Madison, WI, 53706, United States of
America, , Oguzhan Alagoz, Elizabeth Burnside
Breast cancer overdiagnosis issue becomes more severe every year, a recent study
approximates the annual cost of overdiagnosis to the United States as $243
million. We propose a large-scale MDP model to determine the optimal diagnostic
strategy under the consideration of overdiagnosis by incorporating cytologic grade
into the traditional breast cancer diagnostic decision problem.
MD19