2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
239
TA19
106B-MCC
Optimization Modeling and Beyond with a Focus
on Practice
Sponsored: Computing
Sponsored Session
Chair: Leo Lopes, SAS, SAS Campus Drive, Cary, NC, 27513,
United States,
1 - Optimization Modeling With Python Using Pandas
Irv Lustig, Princeton Consulting,
The Python library pandas ) is a popular library used by
data scientists to carry out an entire data analysis workflow in Python. When
building optimization models, we often work with data in tables that are sourced
from databases, CSV files, and spreadsheets. pandas provides a uniform
environment for working with data tables with a large number of methods for
manipulating tabular data, many of which are directly applicable for building
large scale optimization models. In this talk, we will illustrate some of these
powerful features that can accelerate optimization model development and
deployment.
2 - SAS/OR Value Beyond the Model
Leo Lopes, SAS Institute,
We focus on uses of SAS/OR that go beyond modeling and solving, but are are
just as essential to deliver production quality prescriptive analytics models quick-
ly. The tasks we describe support testing, instance generation, access to alterna-
tive solvers, data manipulation, algorithmic control, and visualization.
3 - Building Optimization-enabled Applications Using AMPL Api
Robert Fourer, AMPL Optimization Inc., 2521 Asbury Ave,
Evanston, IL, 60201, United States,
We describe how to combine the power of the AMPL modeling system and a
general-purpose programming language to build rich optimization-enabled client
applications. Having an optimization model expressed in a high-level declarative
form with model and data separation facilitates its evolution and maintenance,
and makes switching between different solvers and data sources easy. At the same
time it is possible to use a familiar development environment and have access to a
wide variety of programming libraries for data management and interface
development.
4 - Decision Optimizer 7.0: Combinatorial Optimization For
Business Analysts
Susanne Heipcke, FICO, San Jose, CA, United States,
, Livio Bertacco, Sebastien Lannez
With Decision Optimizer a strategy analyst can define and optimize complex
decision problems using an intuitive graphical workflow. By designing the
interactions between decisions and constrained metrics, it is possible to create
models for optimizing the assignment of actions, such as investment options or
transaction authorization, for large-scale datasets of input elements leveraging
analytic techniques for sampling and segmentation. In the new version 7.0, DO
has been integrated with FICO Optimization Modeler to provide a more
collaborative, web and cloud based experience, improved scenario management,
distributed execution and Tableau based reporting.
TA20
106C-MCC
Mathematical Finance, Models, Simulation and
Today’s Pressing Problem
Invited: Tutorial
Invited Session
Chair: Joseph M. Pimbley, Maxwell Consulting, LLC, 1, Croton-on-
Hudson, NY, 10520, United States,
1 - Mathematical Finance, Models, Simulation And Today’s
Pressing Problem
Joseph M. Pimbley, Maxwell Consulting, LLC, a, Croton-on-
Hudson, NY, 10520, United States,
Financial markets are awash in information ranging in form from numerical data
to unstructured news reports to nebulous narratives of executives and regulators.
Investors, fiduciaries, intermediaries and other “market actors” apply an
exceedingly broad spectrum of human skill and ingenuity to the interpretation of
this streaming information. Mathematical techniques and analysis, in particular,
are notable tools in which mathematical advances and discoveries may improve
markets’ liquidity, efficiency and pace. This article outlines the origin and
techniques of mathematical finance and associated models and simulations. We
note strengths and shortcomings of these mathematical tools. The greatest
challenge today is to learn and teach to the financial world the necessary
judgment to avoid and rescind destructive deployment of financial models.
TA21
107A-MCC
Beyond Predictive Analytics
Sponsored: Health Applications
Sponsored Session
Chair: Margret Bjarnadottir, University of Maryland, 4324 Van
Munching Hall, College Park, MD, 20742,
1 - Data-driven Specification Of Cyclical Arrival Processes
Donald Lee, Yale University, 165 Whitney Ave, Box 208200,
New Haven, CT, 06520, United States,
Ningyuan Chen, Sahand Negahban
The arrival processes of real-world systems usually exhibit cyclical behaviour. For
example, patient arrivals to emergency departments often peak around midday
and drops off at night. In this talk we show how this periodic structure can be
exploited to obtain a compact and analytic description of the underlying arrival
process from data. Such a model is clearly useful for both simulation and
modeling purposes. We demonstrate the method on arrivals data from an
Emergency Department in southern Connecticut.
2 - Incorporating Dose-prediction Within A Personalized
Treatment Paradigm
Eva Lee, Georgia Institute of Technology,
Xin Wei
This work is joint with Grady Health Systems and Atlanta VA Medical Center. We
design an outcome-based decision support tool that couples a predictive
treatment-effect model with a planning optimization model. The predictive model
uncovers treatment effect analysis of anti-diabetic drug dosage and the blood
glucose level recorded in the titration period of each patient. This evidence is then
incorporated within a personalized planning model for optimal treatment design.
The decision support tool allows continuous learning of evidence for each patient
as new treatment outcomes are recorded.
3 - Optimal Selection Of Health Care Providers
Jerry Kung, MIT,
Given electronic health claims data for employees of a company, we propose a
mixed integer optimization approach to select a collection of providers that
optimizes over total cost, while maintaining quality and respecting travel distance
for employees. We demonstrate that our formulation is tractable for large datasets
and present the computational results on a real claims dataset. By following the
prescriptions generated by our optimization model, we estimate that cost
reductions of up to 10% can be achieved by reassigning patients for a small
number of different types of procedures. We demonstrate that these cost
reductions are robust to changes in a variety of parameters.
TA22
107B-MCC
Joint Session MSOM-HC/HAS: Modeling and
Optimization for Chronic and End-Stage
Renal Disease Patients
Sponsored: Health Applications
Sponsored Session
Chair: Murat Kurt, Merck, Merck, Philadelphia, PA, 07033,
United States,
Co-Chair: David Kaufman, University of Michigan-Dearborn, 19000
Hubbard Dr, Dearborn, MI, 48126, United States,
1 - Optimal Decision Making In A Markov Model With Parameter
Uncertainty: The Case Of Chronic Kidney Disease
Reza Skandari, University of British Columbia,
Steven Shechter
We investigate a Markov decision process whose unknown transition parameters
are revealed partially through state observation. Decisions are made as the state
evolves. We use the model to study the optimal time to start preparing a type of
vascular access for chronic kidney disease patients who will need dialysis.
TA22