2016 INFORMS Annual Meeting Program

42 / 561

INFORMS Nashville – 2016
42
SA86
GIbson Board Room-Omni
Manufacturing I
Contributed Session
Chair: Mohsen Moghaddam, Postdoctoral Researcher, Purdue
University, Grissom Hall, 315 N. Grant Street, West Lafayette, IN,
47907, United States,
1 - Identifying Shifting Production Bottlenecks Using
Clearing Functions
Reha Uzsoy, Professor, North Carolina State University, Dept. of
Industrial & Systems Engg, 300 Daniels Hall Camps Box 7906,
Raleigh, NC, 27695-7906, United States, ,
Baris Kacar, Lars Moench
Production planning models using clearing functions can provide meaningful dual
prices for resources that are not fully utilized. We present a case study of the
analysis of a semiconductor wafer fabrication system using this approach, and
demonstrate the rapidly shifting nature of production bottlenecks even under
stable demand.
2 - Determinants Of Commercial Exploitation For European Funded
Technological R&D In Manufacturing
Vasco Figueiredo Teles, Researcher, MIT Portugal Program, Porto,
Portugal, Abilio P. Pacheco, Abilio P. Pacheco,
Joao Claro, Joao Claro
A significant number of technologies resulting from R&D funded by the European
Commission and aiming at commercial exploitation, do not achieve success in the
marketplace, or in fact even reach it. We use regression analysis and a data set
describing 60 technologies from European R&D projects in manufacturing, to
identify potential determinants of exploitation. The technologies are classified in a
4-stage exploitation scale, and their characteristics (type, sector, geography,
technology readiness level, or platform potential, among others) are compared
among stages. Based on the identified determinants, we offer a set of suggestions
on how to improve exploitation support in these contexts.
Sunday, 10:00AM - 10:50AM
Sunday Plenary
Davidson Ballroom-MCC
Cognitive Computing: From Breakthroughs in the Lab
to Applications on the Field
Plenary Session
Chairs: Chanaka Edirisnghe, Rensselaer Polytechnic Institute,
& Ed H. Kaplan, Yale University and INFORMS 2016 President
1 - Cognitive Computing: From Breakthroughs In The Lab To
Applications on The Field
Guru Banavar, Vice President, IBM Research, Watson Research
Center, Yorktown Heights, NY, United States,
In the last decade, the availability of massive amounts of new data, the
development of new machine learning technologies, and the availability of
scalable computing infrastructure, have given rise to a new class of computing
systems. These “Cognitive Systems” learn from data, reason from models, and
interact naturally with us, to perform complex tasks better than either humans or
machines can do by themselves. These tasks range from answering questions
conversationally to extracting knowledge for discovering insights to evaluating
options for difficult decisions. These cognitive systems are designed to create new
partnerships between people and machines to augment and scale human
expertise in every industry, from healthcare to financial services to education.
This talk will provide an overview of cognitive computing, the technology
breakthroughs that are enabling this trend, and the practical applications of this
technology that are transforming every industry.
Sunday, 11:00AM - 12:30PM
SB01
101A-MCC
Machine Learning
Sponsored: Data Mining
Sponsored Session
Chair: Cynthia Rudin, MIT, 100 Main Street, Cambridge, MA, 02142,
United States,
1 - Generalized Inverse Classification
Michael Lash, University of Iowa, Iowa City, IA, United States,
Qihang Lin, Nick Street,
Jennifer Robinson, Jeffrey W Ohlmann
Inverse classification (IC) is the process of perturbing a test point such that the
predicted probability of a specific class is minimized. In previous work, we
outlined an IC framework that incorporated a linear cost function and solved the
problem by assuming the classifier was differentiable. In this talk we extend the
framework to non-linear costs and relax our assumptions. We demonstrate that,
using heuristic-based methods, the IC problem can be solved using arbitrary
classifiers, about which only basic assumptions are made.
2 - On Difference Of Convex Optimization To Visualize a Word Storm
Dolores Romero Morales, Copenhagen Business School,
Emilio Carrizosa, Vanesa Guerrero
In this talk we address the problem of visualizing in a bounded region a set of
individuals, which has attached a dissimilarity measure and a statistical value.
This problem, which extends the standard Multidimensional Scaling Analysis, is
written as a global optimization problem whose objective is the difference of two
convex functions (DC). Suitable DC decompositions allow us to use the DCA
algorithm in a very efficient way. Our algorithmic approach is used to visualize a
dynamic linguistic real-world dataset.
3 - Consensus Based Modeling Using Distributed
Feature Construction
Haimonti Dutta, University at Buffalo,
Inductive Logic Programming can be used as a tool for discovering relational
features for subsequent use in a predictive model. However, such models often do
not scale. In this paper, we address this computational difficulty by allowing
features and models to be constructed in a distributed manner. There is a network
of computational units, each of which employs an ILP engine to construct a small
number of features and build a (local) model. Then a consensus-based algorithm
is learnt, in which neighboring nodes share information to update local models.
For a category of models (those with convex loss functions), it can be shown that
the algorithm will result in all nodes converging to a consensus model.
4 - Regulating Greed In Multi-Armed Contextual Bandits
Stefano Traca, MIT,
Abstract to come
SB02
101B-MCC
Data Mining in Medical and Sociological
Decision Making
Sponsored: Data Mining
Sponsored Session
Chair: Kamran Paynabar, Georgia Institute of Technology,
Atlanta, GA, United States,
1 - Single Stage Prediction With Text Data Using Dimension
Reduction Techniques
Shawn Mankad, Cornell,
Text data is playing an increasingly important role within the business world for
economic analyses and operations management. There are many ways to
summarize and transform unstructured data into actionable insights. We compare
several modern text analysis methods for prediction of economic outcomes to
derive guidelines for researchers and practitioners.
SA86