2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - Classification Based Approach To Nanorod Segmentation In
Scanning Electron Micrographs
Mostafa Gilanifar, Florida State University, Tallahassee, FL,
United States, , Abhishek Shrivastava
Estimation of nanorod morphology from scanning electron micrographs requires
extracting nanorods from the image. This foreground-background segmentation
of nanorods is challenging due to several reasons - low signal to noise ratio, high
degrees of nanorod overlap and shape similarity. In this talk, we present a
classification-based approach to the segmenation problem. We use a
decomposition-based approach to identify nanorod and background image
patterns, which can be discriminated accurately using a trained classifier. We
demonstrate the accuracy of the approach through several examples.
4 - An Order-invariant Cholesky-log-garch Model For Multivariate
Financial Time Series
Xinwei Deng, Associate professor, Virginia Tech, 211 Hutcheson
Hall, 250 Drillfield Drive, Blacksburg, VA, 24060, United States,
Xiaoning Kang, Kam-Wah Tsui, Mohsen
Pourahmadi
Accurate estimation of time-varying covariance matrices is of great importance in
the analysis of financial data. Most existing models are known to break down at
the estimation stage for dimensions larger than ten or so. In this work, we
propose a novel order-invariant Cholesky-log-GARCH model for estimating the
covariance matrix of multivariate time series based on a random sample from a
population of all possible permutations of the p variables. The ensuing
methodology not only provides accurate estimation, but also gives accurate
prediction at future time points. The merits of the proposed method are illustrated
through three real financial data sets in comparison with conventional methods.
TC68
Mockingbird 4- Omni
Reliability Evaluation and Optimization from
Complex Systems II
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Eunshin Byon, University of Michigan, College Station, MI,
United States,
Co-Chair: Qingyu Yang, Wayne State University, Detroit, MI, United
States,
1 - Reliability Estimation Of Systems With Spherically
Distributed Units
Jingbo Guo, Rutgers, The State University of New Jersey,
, Elsayed A. Elsayed
Spherically distributed systems are emerging in a diverse range of industries such
as aerospace, nuclear, military and oceanography. We refer this kind of units on a
spherical arrangement as k-n-i: G balanced systems. In such a system, n pairs of
units are distributed evenly on i vertical planes in a sphere. The system is
considered functioning when at least k out of n pairs of units operate properly
while satisfying the system’s balance requirement. In this presentation, we
introduce an efficient and general algorithm to estimate the reliability of such
systems for any range of k, n, i. We also present a numerical example to illustrate
the algorithm.
2 - Condition-based Selective Maintenance Optimization For A
Large-scale System
Young Myoung Ko, Pohang University of Science and Technology,
, Eunshin Byon
We extend our previous study on condition-based maintenance optimization that
schedules maintenance activities in a large-scale system comprising identical
units. Our previous method was based on the assumption that each maintenance
activity renews all units, which made the analysis tractable by taking advantage of
the results from the renewal theory, but indeed restricts the applicability of the
method. In this talk, we present a new approach that relaxes the renewal
assumption and finds the optimal thresholds that trigger maintenance operations
to repair a subset of units (i.e., highly deteriorated or failed units) in a system.
3 - A Two-stage Structural Degradation Modeling In Sparse Datasets
Abdallah Chehade, University of Wisconsin - Madison,
, Kaibo Liu
Degradation modeling has become essential to the field of condition monitoring
for better logistics and decision-making. Existing approaches assume there exists a
high-quality data-rich environment. However, in many scenarios, the provided
dataset is sparse with limited observations. For example, patients tend to skip
their semi-annual clinic visits and result in a highly sparse dataset. To fill the
literature gap, we propose a novel two-stage approach for structural degradation
modeling in sparse datasets. Both simulation and case studies that involve a
dataset (ADNI) for Alzheimer disease were used to numerically evaluate and
compare the performance of the proposed methodology.
4 - Title: Resistance Level Determination At The Reliability-based
System Design
Qiyun Pan, University of Michigan,
Eunshin Byon
In order to provide a guideline for choosing appropriate design parameters to
meet a required level of system reliability, resistance level determination becomes
crucial in many applications. At the design stage, resistance level can be estimated
using stochastic simulations, and the resistance level estimation can be formulated
as a statistical quantile estimation problem. We present a new adaptive
importance sampling algorithm to improve the estimation accuracy, given a
computational budget.
TC69
Old Hickory- Omni
Airports, Runways, and Descents
Sponsored: Aviation Applications
Sponsored Session
Chair: Emad Alharbi, NJIT, 8 Gordon Cir, Parsippany, NJ, 07054,
United States,
1 - An Airport Scheduling Mechanism Based On Efficiency, Equity
And On-time Performance Objectives
Alexandre Jacquillat, Assistant Professor of Operations Research,
Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA,
15213, United States, , Vikrant Vaze
Airport congestion can be mitigated through scheduling interventions that control
imbalances between peak-hour demand and capacity. We design and optimize a
non-monetary scheduling mechanism that starts with scheduling inputs from the
airlines and airport capacity estimates, and that reschedules flights based on
efficiency, inter-airline equity and on-time performance objectives. Theoretical
and computational results suggest that large equity gains can be achieved at no,
or small, losses in efficiency, and that accounting for airline preferences can
enhance the mechanism outcomes.
2 - Integration Of Airport Runway And Taxi Planning
Giuseppe Sirigu, ETS Ingeniería Aeronáutica y del Espacio, Dep
Matemática Aplicada Ingenieria Aeroespaci, Plaza Cardenal
Cisneros 3, Madrid, 28040, Spain,
John-Paul Clarke, Angel Marin
We develop and compare network flow-based and Monte Carlo sampling-based
deterministic algorithms for the joint optimization of runway and taxiway
operations considering required separation minima and environmental concerns.
The objective is to minimize the movement time and/or taxi delay considering the
desired takeoff time windows and earliest pushback times. The algorithms are
compared using numerical simulations that replicate real-word airport operations.
3 - Departure Queue Management - A Data Driven Analysis
Marc Rose, Senior Operations Research Analyst, MCR Federal,
LLC, 600 Maryland Ave, 306E, Washington, DC, 20024,
United States,
At the FAA the Terminal Flight Data Manager (TFDM) is composed of many
capabilities. A major component is the Departure Queue Manager (DQM) which
is designed to shift aircraft taxi-out delay away from the runway queue to either
the gate (preferred) or a designated waiting area, thus saving fuel. In this paper I
discuss the databases and calculations applied to estimate the amount of time that
can be shifted from the queue. This will include some discussion of the
programming and constraints required to capture some of the uncertainties in the
concept
4 - Continuous Descent Arrival Adoption During High Traffic Periods:
A Data-driven And Predictive Modeling Approach
Emad Alharbi, PhD Candidate, New Jersey Institute of Technology,
Newark, NJ, 07102, United States,
Layek Abdel-Malek
This study investigates Continuous Descent Arrival (CDA) adoption during high
traffic levels periods. We utilize data-driven system approach and predictive
analytics to build an online CDA predictive model for an enhanced Air Traffic
Management (ATM) procedures as well as an efficient CDA adoption. A
Hierarchical Clustering Analysis (HCA) is performed to aggregate data from offline
flight tracking logs and Meteorological Aviation Reports (METARs) at selected
U.S. airports. The analysis facilitates the visualization of descent profiles and
assists in developing a predictive model for CDA instances using Decision Trees
with AdaBoost and Support Vector Machines (SVM).
TC68