2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
476
3 - How Understanding The Sensitivity And Stability Of Preferences
Among Colorectal Cancer Screening Alternatives Could Lead To
“better” Medical Decisions
M. Gabriela Sava, Assistant Professor, Clemson University,
Clemson, SC, United States, Luis G Vargas,
Jerrold H May, James G Dolan
Patients are faced with multiple alternatives when selecting the preferred method
for colorectal cancer screening, and there are multiple criteria to be considered in
the decision process. We model patients’ choices using a multi-criteria decision
model and propose a new approach for characterizing the idiosyncratic preference
regions for individual and group of patients. We show how insights derived from
the sensitivity and stability of patients’ preferences could be used within the
medical decision making process.
4 - Quick Anp - A New Approach To Anp Sensitivity Analysis
Elena Rokou, Chief Research Officer, Creative Decisions
Foundation, Pittsburgh, PA, 15213, United States,
Bill Adams
The proposed approach consists of two phases, in the first one each decision
maker fills out a very short version of the ANP questionnaire. This way the initial
point of views are collected by the negotiator. The initial questionnaires give the
needed input to define what are the points of greater conflict and which
judgments have a primary role in the final decision outcome. In the second phase
the team focuses only on those conflicting points that have great impact on the
outcome. The focal point of this work is to present a new type of sensitivity
analysis for single level ANP models.
WD66
Mockingbird 2- Omni
Data Analytics For System Improvement III
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Abdallah A Chehade, University of Wisconsin-Madison,
Madison, WI, United States,
Co-Chair: Kaibo Liu, University of Wisconsin-Madison, 1513 University
Ave, Madison, WI, 53706, United States,
1 - Sensory-updated Failure Threshold Estimation For Remaining
Useful Life Prediction
Abdallah A Chehade, University of Wisconsin-Madison,
, Kaibo Liu
The rapid development of sensor technology led to significant research efforts in
remaining useful life (RUL) prediction. Such efforts often consider that a unit fails
when it crosses a failure threshold, which is estimated offline. Unfortunately, such
failure threshold estimation may not be valid due to the stochastic nature of the
underlying degradation mechanism. In this talk, we propose a novel data fusion
model that combines the information from the degradation profiles of historical
units and the in-situ sensory data from an operating unit to online estimate and
update the failure threshold distribution of this unit in the field. This approach is
expected to help better online predict the RUL.
2 - Hard Failure Prediction Based on Joint Models With
Extended Hazard
Jianing Man, City University of Hong Kong, Kowloon, Hong Kong,
, Qiang Zhou
Remaining useful life (RUL) prediction is essential for the prognostics and health
management (PHM) to guarantee the system performance. We use joint models
for the individual units (or systems) which subject to hard failure, including the
random effects model for degradation signals and the extended hazard (EH)
model for time-to-event data. The EH model is a general model that includes the
proportional hazards (PH) model and accelerated failure time (AFT) model as
special cases. A two-stage method and a Bayesian updating scheme are used in
the offline estimation and online prediction separately.
3 - To Integrate Or Not? Covariance Selection In Gaussian
Process Modeling
Ran Yang, Northwestern University,
, Daniel Apley
Power exponential, Gaussian, and Matern are the most commonly used
covariances for Gaussian process modeling of simulation response surfaces. A
recently proposed class of fundamentally different, integrated covariance
functions has been shown to work remarkably well for simulation models of
many real physical systems. We demonstrate that likelihood and leave-one-out
cross validation can both reliably select the best covariance model for a given
response surface and data set and, in particular, determine whether an integrated
covariance function should be used.
4 - Diagnostic Monitoring And Fault Diagnosis In Large Scale
Multivariate Process Via Compressive Sensing And
Optimization Screening
Yan Jin, University of Washington, , Shuai Huang
Smart manufacturing has been an emerging concept in many industries that
highlights unprecedented connectivity of manufacturing infrastructure and
abundance of sensors for real-time monitoring of many system entities. While it
provides a data-rich environment, how to effectively model the variations of
system entities and synthesize decentralized information into global situational
awareness have been challenging issues. To tackle these challenges, we propose
an integrated framework that unifies multivariate statistical monitoring,
compressive sensing, and convex optimization. The advantages of proposed
method are demonstrated through both simulations and real world application.
WD67
Mockingbird 3- Omni
Dynamic Maintenance/Reliability Planning
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Anahita Khojandi, UTK, Address, City, TN, 00000, United States,
Co-Chair: Murat Kurt, Merck & Co., Inc., Addrress, Pittsburgh, PA,
United States,
1 - Combined Condition-based Maintenance And Repairman
Routing Optimization
Bram de Jonge, University of Groningen, Groningen, Netherlands,
, Lisa M Maillart, Oleg A Prokopyev
Existing studies on maintenance optimization for multiple machines generally
ignore the required travel times to move from one machine to another. We
consider the problem of a single repairman who is responsible for the
maintenance activities of a set of geographically distributed machines with
condition monitoring. The problem is formulated as a Markov decision process
and insights are obtained on when to relocate and when to carry out preventive
and corrective maintenance activities.
2 - Maintenance Of Degrading Servers Stored In A Stack
Mahboubeh Madadi, Louisiana Tech, ,
Lisa M Maillart, Charles Richard Cassady, Shengfan Zhang
Inspired by queueing systems in which the servers are stored in a stack and
arriving customers are served by the server on the “top” of the stack, we consider
an M/M/n/n queue under a Most Recently Busy (MRB) service discipline in
which the operating cost of each server increases in its cumulative time-in-use.
More specifically, we formulate a continuous time Markov model to characterize
the transient utilization of each server and to determine optimal maintenance
policies of various forms.
3 - Condition-based Repair Prioritization In Repairable Inventory
Supply Chains
Chiel van Oosterom, Eindhoven University of Technology,
Eindhoven, Netherlands,
Joachim Jacob Arts, Geert-Jan Van Houtum
We propose a model for exploiting condition information to dynamically prioritize
repairs in a capacitated repair shop. The repair shop supports a system with a
number of different repairable components. The system is down whenever a
component fails and no ready-for-use spare part is available for that component.
The objective in prioritizing repairs is to maximize the long-run availability of the
system.
4 - Joint Optimization Of Replacement And Inspection Decisions For
Two-unit Standby Redundant Systems With Non-silent System
Failures
Anahita Khojandi, University of Tennessee, Knoxville, TN,
United States, , Murat Kurt
We consider a two-unit standby redundant system in which individual unit
failures are silent, but simultaneous unit failures cause system shutdown. We
propose a Markov decision process to jointly determine inspection frequency and
preventive repair decisions to minimize the total expected operational cost,
including inspection, repair and failure costs. We analytically establish properties
of the value function and the optimal policy, derive insights from a wide range of
numerical examples, perform extensive sensitivity analysis, and discuss
algorithmic enhancements.
WD66