Informs 2015

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INFORMS Philadelphia – 2015
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3 - Use of Analytics to Investigate Crash Related Risk Factors
Dursun Delen, Oklahoma State University,
408 Business Building, Stillwater, OK, United States of America,
Investigation of the risk factors that contribute to the injury severity in motor
vehicle accidents has proved to be an interesting and challenging problem. In this
study, employing a data-driven predictive analytics methodology along with
information fusion-based sensitivity analyses, we identified the relative
importance of the crash related risk factors as they relate to varying levels of
injury severity.
4 - Real-time Schedule Recovery in Liner Shipping with Regular
Uncertainties and Disruption Event
Chen Li, Dr, Hong Kong University of Science and Technology,
Dept of IELM, Clear Water Bay, Hong Kong, Hong Kong - PRC,
Dongping Song, Xiangtong Qi
We study real-time schedule recovery policies for liner shipping under regular
uncertainties and the emerging disruption. One important contribution is to
distinguish two types of uncertainties, and propose different strategies to handle
them. For regular uncertainties, we address the problem as a stochastic control
problem, and develop the structural results; then we show how an emerging
disruption changes the control policies. Numerical studies demonstrate the
advantages of control policies.
5 - Cyber Physical Allocation to Make Efficient Bike
Sharing Programs
Subasish Das, Research Associate, University of Louisiana at
Lafayette, P.O. Box- 44886, Lafayette, LA, 70504,
United States of America,
Real-time allocation helps making the bike sharing programs efficient and
productive. Cyber physical network of any bike sharing program will provide
real-time status of the bike kiosks and user location. The bike sharing program
can turn these information into real-time data product app. Users can use the app
for the real-time info and make plan accordingly. This paper develops simulation
tool to verify the research findings.
WE72
72-Room 203A, CC
Physical and Computer Experiments
Sponsor: Quality, Statistics and Reliability
Sponsored Session
Chair: Matthew Plumlee, University of Michigan, 1
205 Beal Avenue, Ann Arbor, MI, 48109, United States of America,
1 - Partial Aliasing Relations in Mixed Two- and Three-Level Designs
Arman Sabbaghi, Assistant Professor Of Statistics, Purdue
University, Department of Statistics, 150 N. University Street,
West Lafayette, IN, 47907, United States of America,
Indicator functions are constructed under the orthogonal polynomial
parameterization of contrasts, and applied to the study of partial aliasing, for
mixed two- and three-level designs. The algebra behind calculation of indicator
function coefficients is proven to be a product of individual algebraic operations
for the different types of factors. Conditions for estimable interactions in mixed-
level designs are established by means of this equivalence.
2 - Local Calibration of Computer Models
Arash Pourhabib, Assistant Professor, Oklahoma State University,
322 Engineering North, Stillwater, OK, 74078, United States of
America, , Rui Tuo, Jianhua Huang,
Yu Ding
We propose a framework for the local calibration of parameters when a computer
model is used to approximate a physical process. The proposed framework, non-
parametric local calibration, acknowledges the functional dependency of
parameters on control variables. We present the model in terms of a regularized
optimization problem and solve it using a representer’s theorem. We also prove
the consistency of the estimator obtained via this approach.
3 - Maximum Projection Designs for Computer Experiments
Evren Gul, PhD Student, Georgia Institute of Technology,
251 10th Street NW, THB 504, Atlanta, GA, 30318,
United States of America,
Space-filling properties are important in computer experiments. Maximin and
minimax distance designs consider only space-filling in the full-dimensional
space; this can result in poor projections onto lower-dimensional spaces, which is
undesirable when only a few factors are active. Latin hypercubes can improve
one-dimensional projections but cannot guarantee good space-filling in larger
subspaces. We propose maximum projection designs that maximize space-filling
properties in all subspaces.
4 - Smoothing The Bumps: Sigmoidal Versus Localized Basis
Functions in Gaussian Process Modeling
Daniel Apley, Professor, Northwestern University,
2145 Sheridan Road, Evanston, IL, 60208,
United States of America,
In Gaussian process (GP) modeling of computer simulation data, common
covariance models have localized basis functions, which can result in a bumpy
fitted response surface. We propose a new class of covariance models that can be
viewed as incorporating an integrator into any stationary GP (akin to the
integrator in an ARIMA model), thereby resulting in sigmoidally-shaped basis
functions. We contrast local versus sigmoidal basis functions and argue the
advantages of the latter in GP modeling.
WE73
73-Room 203B, CC
Reliability Test Design
Sponsor: Quality, Statistics and Reliability
Sponsored Session
Chair: Edward Pohl, University of Arkansas, Department of Industrial
Engineering, Fayetteville, United States of America,
1 - Algorithms for Optimal Allocation of Resources in Reliability
Growth Testing
Kelly Sullivan, Assistant Professor, University of Arkansas,
Fayetteville, AR, 72701, ,
Mohammadhossein Heydari
Reliability growth testing seeks to identify and remove failure modes in order to
improve the reliability of a product entering the market. We develop and test a
suite of exact and heuristic algorithms for allocating limited testing resources
within a series-parallel system in order to maximize the resulting system’s
reliability.
2 - Application of Markov Decision Processes for Optimization of
Reliability Growth
Tom Talafuse, Graduate Student, University of Arkansas,
Fayetteville, AR, 72701, United States of America,
, Shengfan Zhang, Edward Pohl
Reliability growth occurs when failure modes are identified and corrective actions
taken to improve system reliability. Planning methods allow construction of
idealized growth curves to estimate the time and resources needed to reach a
desired level of reliability. Since developmental testing results often deviate from
this idealized curve, we propose a Markov Decision Processes approach to
optimally allocate resources to improvement efforts to minimize deviation from
idealized growth.
WE74
74-Room 204A, CC
Reliability IV
Contributed Session
Chair: Minjae Park, Hongik University, 72-1 Sangsu-Dong,
Mapo-Gu, Business School, Seoul, 121-791, Korea, Republic of,
1 - Optimal Condition-based Imperfect Maintenance Policy for
Systems Subject to Multiple Competing Risks
Sara Ghorbani, American Express, 33 Hudson Street, Jersey City,
NJ, 07302, United States of America, ,
Elsayed A. Elsayed, Hoang Pham
We develop a generalized threshold-type condition-based maintenance (CBM)
policy for a system subject to multiple competing risks including degradation
process and sudden failure. This model extends the existing research by
considering imperfect maintenance. Furthermore, a special case of a system
subject to two independent competing risks, degradation and sudden failure is
studied and the numerical optimization analyses are presented.
2 - Simulation-based Reliability Evaluation of Multi-stage Multi-state
Manufacturing Systems
Seyed Niknam, Western New England University,
1215 Wilbraham, Springfield, MA, 01119, United States of
America, , Rogerio Peruchi
This research investigates the reliability analysis of a multi-stage multi-state
manufacturing system. The proposed model provides a sensible measure to assess
the system situation against the best-case scenario of a production line. The
proposed model incorporates not only failures that stop production but also deals
with partial failures where the system continues to operate at reduced
performance rates. A simulation model is developed to define the possible states
in the system.
WE72