2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - Stabilizing Gradient Enhanced Kriging With Sparsity Constraints
Peter Qian, University of Wisconsin,re
Gaussian processes are widely used for emulating computer simulations. It is
known that the use of partial derivative information can dramatically improve
function estimation. However, the use of partial derivative information comes at
the cost of high numerical instability. We investigate an approach to mitigate this
instability by exploiting the possibility that some partial derivatives may introduce
enough error due to numerical instability to significantly degrade predictive
accuracy. Experimental results indicate this procedure can dramatically reduce
numerical error in interpolation. Applications to model calibration will also be
discussed.
3 - Model Calibration With Censored Data
Fang Cao, Georgia Institute of Technology, Atlanta, SGA, United
States, , Shan Ba, William A Brenneman,
Roshan Joseph
The purpose of model calibration is to make inference about the unknown
parameters of a computer model. The Kennedy-O’Hagan approach is widely used
for calibration which accounts for the inadequacy of the computer model while
simultaneously estimating the calibration parameters. In many applications
censorship occurs when exact outcome of the physical experiment is not observed
but is known to fall within a certain region. In such cases KO approach cannot be
used directly and we propose a method to incorporate the censoring information
when performing calibration. The method is applied to study the stability of liquid
and the results show significant improvements over traditional methods.
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Mockingbird 3- Omni
Maintenance and Reliability Planning
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Murat Kurt, Merck & Co, Inc, 351 N. Sumneytown Pike,
North Wales, PA, 19454, United States,
Co-Chair: Anahita Khojandi, University of Tennessee,
1 - Optimal Design Of Hybrid Sequential Testing For A System With
Mixtures Of One-shot Units
Yao Cheng, Rutgers University, Department of Industrial &
Systems Engineering, Piscataway, NJ, 08809, United States,
, Elsayed Elsayed
Non Destructive Testing is conducted to determine the functionality of the units
without permanent damage in order to estimate the units’ reliability. In this
presentation, we investigate a system composed of non-identical units with
different characteristics and subjected to hybrid reliability testing (Destructive and
NDT). It is of interest to optimally design the hybrid sequential reliability testing.
After conducting a number of hybrid testing, we decrease the sample size of the
destructive testing as the accuracy of reliability metrics estimation improves.
Eventually, we only need to conduct NDT only. The efficiency and accuracy of the
proposed methods are validated.
2 - Wind Farm Replacement In A Markov Modulated Environment
David Abdul-Malak, University of Pittsburgh,
Jeffrey P. Kharoufeh
In this talk we will present a model for jointly replacing wind turbine components
in a wind farm setting. Components are assumed to degrade in a shared,
exogenous, Markov modulated environment. Continuous state variables and a
high dimensional state space cause the problem to be computationally intractable.
To overcome these complications, structural results are proven and a
reinforcement learning (RL) approach is employed.
3 - An Enhanced Copula-based Prognosis For Proactive
Maintenance Of Lithium-ion Batteries
Zhimin Xi, University of Michigan-Dearborn,
Data-driven prognostics typically requires sufficient offline training data sets for
accurate remaining useful life (RUL) prediction for the purpose of proactive
maintenance of engineering products. We investigate performances of typical
data-driven methodologies when the amount of training data sets is insufficient to
better understand the methodology limitation. An enhanced copula-based
approach is specifically developed for the scenarios with insufficient run-to-failure
training data sets. RUL prediction of lithium-ion batteries in terms of the capacity
degradation is employed for the demonstration.
4 - Optimizing Periodic Inspection Frequencies For a Class Of
Stochastically Degrading Systems
David Kaufman, University of Michigan-
Dearborn, Dearborn, MI, United States, ,
Mahboubeh Madadi, Murat Kurt
We consider existing models that optimize repair-replacement decisions for
systems the degradation status of which follow a discrete time Markov chain over
a set of finite states and can be revealed only by costly inspections. Given worse
conditions imply higher operation costs, we utilize first-order stochastic
dominance relationship among the powers of IFR-structured degradation matrices
to propose approximately-optimal periodic inspection decisions that minimize the
total expected discounted cost due to operation, repair and inspection. We
illustrate our approach through numerical examples.
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Mockingbird 4- Omni
Panel: IOT-enabled Data Analytics: Opportunities,
Challenges and Applications
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Moderator: Kaibo Liu,
1 - LoT-enabled Data Analytics: Opportunities, Challenges
And Applications
Kaibo Liu, University of Wisconsin - Madison,
The goal of this session is to push the frontier in IoT application and the enabled
data analytics research. The session provides a forum where participants can
describe current opportunities, identify important problems and areas of
application, explore emerging challenges, and formulate future research
directions.
2 - LoT And Data Analytics
Tobin Jansenberger, American Family
Insurance,
3 - LoT Analytics
Rong Duan, AT&T,
4 - LoT Data Analytics
Subrat Sahu, Caterpillar Inc,
5 - LoT Data Analytics
Gul Ege, SAS,
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Old Hickory- Omni
Game Theory and Competitive Applications
Sponsored: Military Applications
Sponsored Session
Chair: Brian J Lunday, Assistant Professor, Air Force Institute of
Technology, 2950 Hobson Way, WPAFB, OH, 45433,
United States,
1 - 1 Vs. (n-1) Modeling For Project Scheduling Interdiction
Zachary Little, The Perduco Group, 3610 Pentagon Boulevard
#110, Beavercreek, OH, 45431, United
States,
A bilevel programming problem is developed for a one-to-many game involving
project scheduling interdiction. As a coalition, the many (n-1) adversaries aim to
minimize the total cost of a set of project schedules given a time/cost trade-off.
The single interdictor aims to maximize this same total cost for the coalition’s
project schedules. The modeling framework and use of duality are discussed, with
emphasis placed on coalition interaction for this study. Initial results examine the
impact of player perceptions on interdictor and coalition decisions.
2 - Approximate Dynamic Programming For Missile Defense
Interceptor Fire Control
Matthew J Robbins, Air Force Institute of Technology, Wright-
Patterson AFB, OH, United States, ,
Michael T Davis, Brian J Lunday
A missile defense system must protect assets against multiple offensive missile
salvos over time. The defender must determine how many interceptors to fire at
each incoming missile. We develop a Markov decision process (MDP) model to
determine optimal fire control policies. Approximate dynamic programming
(ADP) is utilized to explore the efficacy of applying approximate methods to the
problem. We obtain policy insights by analyzing subsets of the state space that
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