2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - Analysis on Energy Efficient Switching Of Machine Tool With
Stochastic Arrivals And Buffer Information
Andrea Matta, Shanghai Jiaotong University,
Energy saving in production plants is becoming more and more relevant due to
the pressure from governments to contain the environmental impact of
manufacturing, and from companies to reduce costs. One of the measures for
saving energy is the implementation of control strategies that reduce energy
consumption during the machine idle periods. This talk will deal with switching
policies that turn the machine off when production is not critical, and on when
the part flow has to be resumed. A general policy is formalized by modelling
explicitly the energy consumed at each machine state.
MC67
Mockingbird 3- Omni
Panel Discussion on Publishing in Quality and
Reliability: The Editors’ Perspective
Panel Session
Moderator: Kaibo Wang, Tsinghua University, Beijing, China,
1 - Panel Discussion on Publishing In Quality And Reliability:
The Editors’ Perspective
Kaibo Wang, Tsinghua University,
This panel brings journal editors to share their perspectives and experiences with
the audience and answer questions pertaining to publication in Quality,
Reliability and Data Sciences. Panelists are: Dr. Jianjun Shi, IIE Transactions; Dr.
Fugee Tsung, Journal of Quality Technology; Dr. Peihua Qiu, Technometrics; Dr.
Murat Caner Testik, Quality Engineering; Dr. Jing Li, Quality Technology and
Quantitative Management.
2 - Panelist: IIE Transactions
Jianjun Shi, Georgia Institute of Technology,
3 - Panelist: QTQM
Jing Li, Arizona State University,
4 - Panelist: Quality Engineering
Murat Caner Testik, Hacettepe University,
5 - Panelist: Technometrics
Peihua Qiu, University of Florida,
6 - Panelist: Journal Of Quality Technology
Fugee Tsung, HKUST,
MC68
Mockingbird 4- Omni
Reliability Evaluation and Optimization from
Complex Systems I
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 - A Space-time Autoregressive Model For Radar Images Under A
Lagrangian Integration Scheme
Xiao Liu, IBM T.J. Watson Research Center, Yorktown Heights, NY,
United States,
This paper is concerned with the spatio-temporal modeling of two dimensional
radar echo fields from a sequence of radar images. The method is useful for many
environment- and energy-related problems. For example, the precipitation
forecast, and the prediction of solar power production.
2 - Reliability Modeling For Continuous-state Systems
Xinying Wu, Ohio University, Tao
Yuan
This talk presents a Bayesian hierarchical modeling framework for modeling the
reliability and degradation of continuous-state systems composed of continuous-
state components. Degradation modeling, degradation data analysis, system
reliability prediction, and component important measures will be discussed.
3 - On The Probabilistic Site Selection Problem
Yiwen Xu, North Dakota State University, Fargo, ND, 58102,
United States, Haitao Liao
In this research, we study a site-selection problem in probabilistic networks where
both nodes and edges are prone to be failed. To enhance the probability of
connectivity from one node to another, options for adding multiple edges (i.e.,
edge-level redundancy) are considered. We formulate the mathematical
programming problem and develop a method to solve the problem. Numerical
examples are provided to demonstrate the problem and the use of the proposed
solution methodology.
4 - A Physical-statistical Hybrid Model For Li-ion Battery Prognosis
Nan Chen, National University of Singapore,
The traditional PHM approaches for Li-Ion batteries relied on the experimental
data, like battery capacity or impedance. We proposed a physical-statistical model
to take full use of operational data, which are readily available, to model and
predict the performance and reliability of Li-Ion batteries. Both numerical and
case studies are constructed to demonstrate the effectiveness and promising
futures of this physical-statistical model in the real applications.
MC69
Old Hickory- Omni
Military Resource Management
Sponsored: Military Applications
Sponsored Session
Chair: Brian J Lunday, Air Force Institute of Technology, P.O. WPAFB,
OH, 1, United States,
1 - Discrete Event Simulation-based Analysis Of Personnel
Evaluation Policy
Lee A. Evans, University of Louisville, Louisville, KY,
United States, , Prajwal Khadgi
The United States Army uses a forced ranking appraisal system, a practice largely
abandoned in the private sector, in evaluating its officer corps. The psychological
aspects of forced ranking evaluation systems have been well documented, but this
study examines the mathematical aspects of how these systems can lead to
misidentification of high-performing individuals. We show how the binomial
distribution can explain many of the challenges, analyze human behavior in such
a system, and create a discrete event simulation to analyze the effects of policy-
driven constraints.
2 - Modeling And Forecasting Army Enlistments With Geographic
Data Weighting, Principal Components Analysis, And
Linear Regression
Joshua McDonald, U.S. Army, Aberdeen Proving Ground, MD,
United States,
Using ordinary least squares regression applied to geographically weighted panel
data we forecast the production of Regular U.S. Army enlistments in 38 recruiting
markets. We find that a set of five continuous independent variables obtained
through principal components analysis plus categorical variables for markets and
quarters of the fiscal year achieves effective 15-month forecasts; when forecasting
independent variables, the models explain between 63% and 73% of the
variation between actual and predicted data at the highest level of aggregation,
depending on enlistment contract type.
3 - Optimal Design Of Piezoelectric Materials For Maximal
Energy Harvesting
Russell Nelson, United States Military Academy, West Point, NY,
10996, United States, , Hong Zhou,
Susan Sanchez
The DoD seeks alternative methods to produce electricity, thus decreasing
dependence on fossil fuels and increasing combat power. Piezoelectric generators
can produce alternative electrical power in isolated and austere conditions. We
use three and six variable mathematical models to analyze piezoelectric generator
power capabilities. Using mk factorial sampling, nearly orthogonal and balanced
Latin hypercube (NOBLH) design, and NOBLH iterative methods, we find
solutions to maximize piezoelectric generator power output. We further analyze
our optimal results using robustness analysis techniques. Our results provide
optimal material parameter and environmental designs.
4 - Risk Assessment In Robust Goal Programming
Robert Hanks, Air Force Institute of Technology,
We investigate interval-based and norm-based uncertainty sets using cardinality-
constrained robustness in the Robust Goal Programming (RGP) construct in
addition to strict robustness using ellipsoidal uncertainty sets. Then, using utility
theory, a decision maker’s (DM) view of risk is quantified via a utility function,
which will be mapped back to relevant parameters of the varying uncertainty sets
to model the DM’s risk attitude toward a robust solution. The findings offer
theoretical contributions to the RGP framework and will be applied in a future
endeavor to setting shipping rates for the United States Transportation
Command’s customers as it pertains to revenue management.
MC69