Informs 2015

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INFORMS Philadelphia – 2015
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3 - Early Warning Methods and Predictive Models for Hospital Risk
and Readmissions
Jakka Sairamesh, Ceo And President, CapsicoHealth, Inc, 2225 E
Bayshore Rd. Ste. 200, Palo Alto, CA, 94303, United States of
America, Ruichen Rong
This poster and research abstracts presents the effectiveness of methods for
improving patient quality outcomes (e.g. reducing 30-day readmissions) based on
clinical and cost based factors. We will present early-warning methods to predict
patients at risk of 30-day readmissions based on past admissions, ER visit rates,
mortality rates, and charges. The dominant factors includee clinical risk, costs,
emergency room visits and mortality rates. The prediction showed nearly 88
percent accuracy.
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38-Room 415, Marriott
Big Data II
Contributed Session
Chair: Christoph Wunck, Professor, Jade University of Applied Sciences,
Friedrich-Paffrath-Str. 101, Wilhelmshaven, 26389, Germany,
1 - Making Billions of Decisions a Day: Experiment Driven Bid
Optimization in Online Advertising
Daizhuo Chen, PhD Candidate / Senior Data Scientist, Columbia
Business School / Dstillery, 470 Park Avenue S, 6th Floor South,
New York, NY, 10016, United States of America,
, Robert Phillips, Garrett Van Ryzin,
Brian D’alessandro, Perlich Claudia
Online advertising is a good playground for operations researchers who want to
explore the opportunities and pitfalls of the “Big Data” promise. This talk will
focus on bid optimization: as an ad buyer, how to determine the best bid prices for
billions of ad opportunities everyday, in today’s dynamic and opaque
marketplaces of real-time bidding. We introduce a solution based on continuous
experimentation and optimization, and touch on a paradox of big data: you never
have the data you need.
2 - Fast Gaussian Process Regression for Large Non-Stationary
Spatial Data
Babak Farmanesh, Oklahoma State University, 322 Engineering
North, Stillwater, OK, 74074, United States of America,
, Arash Pourhabib
We propose Sparse Pseudo-input Local Kriging (SPLK) as a predictive model for
large non-stationary spatial data. SPLK uses orthogonal cuts to create small
domains, where it applies sparse Gaussian process regression. The orthogonal cuts
enable SPLK to be applied to spatial datasets that include exogenous variables,
hence having a dimension greater than three. We apply SPLK to real and
simulated datasets and demonstrate it can efficiently predict the response variable.
3 - Advanced Decision-Making Procedures in Massive
Failure Data Classification
Keivan Sadeghzadeh, Graduate Research Assistant, Northeastern
University, 27 Payne Rd, Newton, MA, 02461, United States of
America, , Nasser Fard
In many professional areas, management decision-making process is based on the
type and size of data where data classification is a necessary procedure. Massive
amount of data in high-dimensions are increasingly accessible from various
sources and it has become more difficult to process the streaming data in
traditional application approaches. This paper presents advanced procedures to
analyze high-dimensional failure data in order to facilitate decision-making
through data classification.
4 - Preprocessing of Manufacturing Process Signals using
Wavelet-based Filters
Christoph Wunck, Professor, Jade University of Applied Sciences,
Friedrich-Paffrath-Str. 101, Wilhelmshaven, 26389, Germany,
Bringing Big Data to the shop floor is one of the current visions to increase
product quality, process reliability and overall productivity in manufacturing.
Extracting the information content hidden in the flood of sampled data of process
variables in real time requires data reduction and filtering techniques that are not
commonly applied in manufacturing processes. A case study on injection molding
shows how wavelet-based data preprocessing can simplify any subsequent data
analysis.
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39-Room 100, CC
Interdisciplinary Focus on Problem Solving
Cluster: Operations/Marketing Interface
Invited Session
Chair: Kathleen Iacocca, University of Scranton, 439 Brennan Hall,
Scranton, PA, United States of America,
1 - A Functional Robust Newsvendor Model with Uncertain Price-
Sensitive Demand
Junxuan Li, Graduate Student Instructor, University of Michigan-
Dearborn, 23935 W Outer Dr. Apt. H16, Melvindale, MI, 48122,
United States of America, , Jian Hu,
Sanjay Mehrotra
A functional robust newsvendor model with coordination of pricing and
inventory decisions in uncertain market is proposed, which specifies an
uncertainty set of nonparametric demand curves and seeks the best decisions
against the worst case. We discuss the impact of functional robustness and
wholesale price and develop a cut generation algorithm for convex demand curve
case, while reformulate the model as second-order conic program for concave
case. A grocery store case study is then discussed.
2 - Supply Chain Robust Optimization: An Examination of the
Relationship Between Flexibility, Agility
Seyed Nooraie, NCAT university, 2205 New Garden Rd #1210,
Greensboro, NC, 27410, United States of America,
Mahour Mellat Parast,
Paul M Stanfield
A robust multi-objective mixed integer nonlinear programming model is defined
for an Aggregate Production Planning (APP) incorporating three conflicting
objective functions simultaneously. We propose a theoretical construct linking
elements of uncertainty with aspects of agility and flexibility where we use
responsiveness to enhance these elements to overcome the future risk.
3 - Spreadsheet Approach for Integrating Production, Marketing, and
Finance Decisions in Aggregate Plans
Kathleen Iacocca, University of Scranton,
439 Brennan Hall, Scranton, PA, United States of America,
Kingsley Gnanendran
The well-known aggregate planning model is extended to explicitly incorporate
demand forecasts as modified by any sales promotions, along with working capital
constraints. The components of the extended model are implemented on a
spreadsheet and linked using VBA programming to demonstrate the ease of
managerial applicability. Optimal decision variables include workforce size;
production, inventory, and outsourcing levels; and the magnitude and timing of
price promotions and infusions of cash.
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40- Room 101, CC
Organization Theory II
Contributed Session
Chair: Amit Das, Associate Professor, Qatar University, P.O. Box 2713,
Doha, Qatar,
1 - Refocusing Business Incubator Research:
The Process of Business Incubation
David Scheaf, UNC Charlotte, 9201 University City Blvd,
Charlotte, NC, United States of America,
Literature on business incubation resembles the early stages of entrepreneurship
research. Research has focused on defining business incubators and the associated
outcomes. The lack of a cohesive framework has resulted in numerous definitions
that have prevented adequate theoretical development. Presented is a
conceptualization of the incubation process and how various theories from the
organizational sciences can inform future research.
2 - Performance in Knowledge Intensive Environments:
Interplay of Worker and Managerial Experience
Juan Pablo Madiedo, PhD Candidate, IE Business School, Calle
Maria de Molina 12, Bajo, PhD Program Office, Madrid, 28028,
Spain,
Aravind Chandrasekaran, Fabrizio Salvador
This study examines the importance of workers and managers as sources of
experience in a knowledge intensive work environment. We analyze the interplay
of workers and managers and its effect on task performance. We use a dataset
with information on over 1500 software maintenance tasks collected from a
global IT and consulting corporation for testing our model.
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