Informs 2015

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INFORMS Philadelphia – 2015
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73-Room 203B, CC
Functional Data Analysis
Sponsor: Quality, Statistics and Reliability
Sponsored Session
Chair: Moein Saleh, Discover Financial Services/ Arizona State
University, 699 S Mill Ave, Tempe, AZ, 85281, United States of
America,
1 - On the Use of Gaussian Processes for Surface and Profile Data
Enrique Del Castillo, Penn State University, Industrial Eng. and
Statistics Depts., State College, United States of America,
Standard applications of Gaussian Processes in manufacturing data have
traditionally been based on models of the form z(x,y) where x,y,z are coordinates
acquired with some sensor, so correlation is assumed to occur on euclidean space
external to the surface. We show new methodology that assumes instead
correlation exists on the intrinsic surface points along geodesic distances, and
show how this leads to better surface reconstruction in both simulated and real
datasets.
2 - Functional Clustering with Applications in Single
Molecule Experiments
Ying Hung,
Cell adhesion experiments refer to biomechanical experiments that study protein,
DNA, and RNA at the level of single molecules. Motivated by analyzing a single
molecule experiment, a new statistical framework is proposed based on functional
clustering approaches. Simulations and applications to real experiments are
conducted to demonstrate the performance of the proposed method.
3 - Design of Experiments for Functional Response
Moein Saleh, Discover Financial Services/ Arizona State
University, 699 S Mill Ave, Tempe, AZ, 85281,
United States of America, Rong Pan
Applications of DOE for single response variable can be seen in nearly every
disciplines in science and engineering. However, there are very few publications
that discussed optimal design for the experiments with multiple responses taken
over different points of a continuum variable. This continuum can be any other
continuous variable for functional data analysis such as time in longitudinal
study. My study focuses on developing a framework for designing the
experiments for functional response.
4 - Monitoring and Diagnostics of High Dimensional
Multi-stream Data
Samaneh Ebrahimi, Research Assistant, Georgia Institute of
Technology, 755 Ferst Drive, Atlanta, GA, 30332, United States of
America, , Kamran Paynabar,
Chitta Ranjan
Correlated high-dimensional data streams (HDDS) pose significant challenges in
Statistical Process Monitoring. In this research, we integrate PCA and Adaptive
Lasso, and propose a novel approach for effective process monitoring and
diagnosis of HDDS. The effectiveness of the proposed approach is validated
through simulation and a case study.
TA74
74-Room 204A, CC
System and Process Informatics in Additive
Manufacturing (I)
Sponsor: Quality, Statistics and Reliability
Sponsored Session
Chair: Linkan Bian, Assistant Professor, Mississippi State University, 260
McCain Building, Mississippi State, Starkville, MS, 39762, United States
of America,
1 - Accelerated Process Optimization for Laser-based Additive
Manufacturing (LBAM)
Amir M. Aboutaleb, Mississippi State University, 260 McCain
Building, Mississippi State, MI, 39762, United States of America,
Linkan Bian, Alaa Elwany, Nima Shamsaei,
Scott M. Thompson
A novel Design-of-Experiment methodology is proposed to efficiently optimize
process control parameters for LBAM by leveraging data obtained from prior
related but non-identical studies. Our method accounts for unavoidable difference
between the experimental conditions of the current and prior studies and
quantify the associated uncertainty, which is further updated using real-world
data generated in the current study.
2 - Concurrent Process Plan Optimization for Additive Manufacturing
Bahir Khoda, Professor, North Dakota State University, Room #
202F Civil and Industrial Enginee, 1410 14th Avenue North,
Fargo, ND, 58102, United States of America,
Amm Nazmul Ahsan, Md Habib
Implementing additive manufacturing processes effectively requires addressing
issues of process proficiencies and resource utilization, both of which have a
strong environmental impact. In this paper, both part build orientation and
material deposition direction are concurrently optimized by analyzing part
geometry to minimize the resource requirement. A concurrent multicriteria
process plan optimization framework is developed using Genetic Algorithms (GA)
technique.
3 - Online Sensor-based Monitoring in Aerosol Jet Printing Process
Prahalad Rao, SUNY Binghamton, 4400 Vestal Pkwy. E,
Binghamton, NY, United States of America,
Roozbeh Salary, Jack Lombardi,
Mark Poliks
Aerosol Jet Printing (AJP) is an additive manufacturing process (AM) is emerging
as a viable method for printing conformal electronics. However, teething quality
related problems in AJP remain unresolved. We propose approaches based on
image processing and sensor data analytics to achieve online quality monitoring
in the AJP process. The effectiveness of the proposed approach is assessed and
evaluated with several real case studies implemented on an aerosol jet printer
setup.
TA75
75-Room 204B, CC
IBM Research Best Student Paper Award I
Sponsor: Service Science
Sponsored Session
Chair: Ming-Hui Huang, National Taiwan University, Taiwan - ROC,
1 - Best Student Paper Competitive Presentation
Ming-Hui Huang, National Taiwan University, Taiwan - ROC,
Finalists of the IBM Research Best Student Paper Award present their research
findings in front of a panel of judges. The judging panel will decide the order of
winners, which will be announced during the business meeting of the Service
Science Section at the Annual Conference.
1- Service Innovation and the Role of Collaboration
Cong Feng, Syracuse University, 721 University Avenue,
Syracuse NY, United States of America, ,
K. Sivakumar
Results show that (1) the effect of service innovation on firm performance is
greater for service firms than manufacturing firms; (2) the relationship between
the propensity for service innovation and three types of collaboration is signifi-
cant; and (3) vertical and third-party collaborations are more beneficial than hor-
izontal collaboration for service firms.
2 - Brand Equity and Extended Service Contract Purchase Decisions
Moein Khanlari Larimi,University of Alberta, Canada,
, Paul Messinger
In this paper, we explore the role of brand equity on consumers’ extended serv-
ice contract (ESC) purchase decisions. We draw from past findings to show that
higher brand equity has an overall positive impact on ESC purchase decisions.
We also explore the positive impact of stores on ESC purchase decisions.
3 - Regulating Greed over Time
Stefano Traca, Massachusetts Institute of Technology, Cambridge,
MA, United States of America, , Cynthia Rudin
In retail, there are predictable yet dramatic time-dependent patterns in customer
behavior, such as periodic changes in the number of visitors, or increases in visi-
tors just before major holidays (e.g., Christmas). The current paradigm of multi-
armed bandit analysis does not take these known patterns into account, which
means that despite the firm theoretical foundation of these methods, they are
fundamentally flawed when it comes to real applications. This work provides a
remedy that takes the time-dependent patterns into account, and we show how
this remedy is implemented in the UCB and e-greedy methods. In the corrected
methods, exploitation (greed) is regulated over time, so that more exploitation
occurs during higher reward periods, and more exploration occurs in periods of
low reward. In order to understand why regret is reduced with the corrected
methods, we present a set of bounds that provide insight into why we would
want to exploit during periods of high reward, and discuss the impact on regret.
Our proposed methods have excellent performance in experiments, and were
inspired by a high-scoring entry in the Exploration and Exploitation 3 contest
using data from Yahoo! Front Page. That entry heavily used time-series methods
to regulate greed over time, which was substantially more effective than other
contextual bandit methods.
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