2016 INFORMS Annual Meeting Program

66 / 561

INFORMS Nashville – 2016
66
3 - Efficient Multi-fidelity Decision Making For Dynamic Data Driven
Application Systems
Jie Xu, George Mason University,
Chun-Hung Chen, Edward Huang
Dynamic data driven application systems enable real-time simulation-based
decision making. However, existing simulation optimization algorithms lack the
computational efficiency required for real-time decision making. In this talk, we
present a Bayesian framework that makes use of data and models of multiple
fidelity levels to achieve the computational efficiency necessary for decision
support in the context of dynamic data driven application systems.
4 - Dynamic Data Driven Modeling Of Nanoparticle
Self-assembly Processes
Xin Li, Florida State University, 2525 Pottsdamer St, Building A,
Suite A231, Tallahassee, FL, 32310, United States,
, Chiwoo Park, Yu Ding, Tao Liu
We present a dynamic data-driven modeling strategy, capable of tracking and
predicting the transient dynamics of nanoparticle production processes. The
proposed methodology is built upon two emerging multi-resolution instruments.
The methodology regularly triggers cheap low resolution measurements while
triggering expensive high resolution measurements when model predictions fail.
The proposed strategy would provide crucial clues to understand nanoparticle
productions as well as powerful insights to control the production of
nanoparticles for yielding desirable morphology.
SB68
Mockingbird 4- Omni
Advanced Maintenance Modeling
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Yisha Xiang, Lamar University, Beaumont, TX, United States,
Co-Chair: David W Coit, Rutgers University, Piscataway, NJ, United
States,
1 - Reordering Of Spare Parts Experiencing Two Phase
Onshelf Deterioration
Haitao Liao, University of Arkansas,
We study maintenance and inventory policies for a system carrying spare parts
that experience two-phase on-shelf deterioration. Based on the parts’ degradation
states, we introduce two different replacement strategies for the spare
consumption, i.e., the Degraded-First strategy and the New-First strategy.
2 - A Model Of System Limiting Availability Under Imperfect
Maintenance
Suzan Alaswad, Zayed University,
Charles Richard Cassady, Edward A Pohl
In this paper, we explore the impact of Kijima Type II imperfect repair model on
equipment availability. Our specific interest is in the system steady-state
availability. Since the derivation of a closed form expression for the limiting
availability is extremely difficult, we use simulation modeling and analysis to
estimate the system limiting availability. Next, we develop a meta-model to
convert the system reliability and maintainability parameters into the coefficients
of the limiting availability estimate without the simulation effort. Lastly, we
identify an optimal age-based preventive maintenance policy that maximizes the
system’s steady-state availability.
3 - Predictive Maintenance For A Multi-unit System
Yisha Xiang, Lamar University,
Zhicheng Zhu, David W Coit
Preventive maintenance has been extensively studied. Time-based PM and
condition-based maintenance (CBM) are two major approaches for PM. However,
time-based PM is often associated with high occurrence of system breakdowns,
and CBM might incur more-than-necessary inspections. Recently, predictive
maintenance has become popular since it aims to pinpoint when a failure is about
to occur and prolong the operational time. However, only a few predictive models
consider a multi-unit system. In this paper, we develop an opportunistic
predictive maintenance structure for a multi-unit system. Numerical examples are
provided to illustrate the proposed predictive maintenance policy.
4 - Reliability Of System With Clusters Of Dependent
Degrading Components
Sanling Song, Rutgers University, Busch Campus,
Core Building Room 201, Piscataway, NJ, 08854, United States,
, David W Coit, Qianmei Feng,
Yisha Xiang
A reliability model is developed for complex multi-component system with each
component subject to multiple failure processes. Degradation paths for certain
components are stochastically dependent with clusters of dependent components.
Gamma process is used to model the stochastic process of component
deterioration. In this new model, two failure processes within each component
are dependent due to simultaneous shared exposure to shock process.
Furthermore, degradation paths among certain components are considered to be
dependent. Components sharing dependent degradation can be determined by
the MLE of model parameters.
SB69
Old Hickory- Omni
Panel Discussion: Internet of Things (IoT)
Data Analytics
Sponsored: CPMS, The Practice Section
Sponsored Session
Moderator: Robin Lougee, IBM Research, IBM TJ Watson Research
Center, 1101 Kitchawan Road, Yorktown Heights, NY, 10598, United
States,
1 - Panel Discussion: Internet Of Things (IoT) Data Analytics
Robin Lougee, IBM Research, IBM TJ Watson Research Center,
1101 Kitchawan Road, Yorktown Heights, NY, 10598,
United States,
What are the opportunities and challenges for analytics and operations research
when virtually every machine that operates in every market and sector can be
connected to the internet? Thought leaders who create IoT technologies, systems,
and application solutions will share their experiences, delineate the substance
from the hype, and engage in a lively discussion of the most needed areas of
future research.
2 - Panelist
Doug Meiser, The Kroger Co., 11450 Grooms Road, Cincinnati,
OH, 45242, United States,
3 - Panelist
Srinivas Bollapragrada, GE Global Research Center, 1 Research
Circle, K1-5a50a, Niskayuna, NY, 12309, United States,
4 - Panelist
Ihsan Sehgal, IBM, 3039 E Cornwallis Road, Research Triangle Park,
NY, 27709, United States,
5 - Panelist
Joseph Byrum, Syngenta, 913 31st Street, West Des Moines, IA,
50265, United States,
SB70
Acoustic- Omni
Transportation, Freight II
Contributed Session
Chair: Samaneh Shiri, Research Assistant, University of South Carolina,
101 pickens st. APt. G2, Columbia, SC, 29205, United States,
1 - Commodity-based Econometric Empty Trip Models
Carlos Alberto Gonzalez-Calderon, Research Associate, Rensselaer
Polytechnic Institute, 4 25TH ST, APT 5, Troy, NY, 12180, United
States, , Jose Holguin-Veras, Ivan Dario Sanchez-
Diaz, Ivan Sarmiento, Johanna Amaya
This paper estimates econometric models of empty trips of different commodities
and vehicle types. In doing this, panel models with time-dependent parameters
and fixed effects are used to assess how parameters change over time considering
different commodities, and to detect the presence of time effects not captured by
the other parameters. The performing of the formulation for the different
commodities is tested in Colombia.
1 - On The Unique Features Of On-demand Peer-to-Peer
Logistics Systems
Jennifer A Pazour, Assistant Professor, Rensselaer Polytechnic
Institute, 110 8th street, CII 5217, Troy, NY, 12180, United States,
On-demand peer-to-peer logistics systems use a business model for the movement
and storage of goods that matches independent supply resources (warehouse
space, truck space, delivery services) to demand requests on demand. These
systems are part of the growing sharing economy and gig economy. We identify
the unique features of these systems, comparing and contrasting them with
traditional logistics systems. By mapping the characteristics to supply chain
principles, we identify challenges with designing and operating, as well as using
SB68