2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - Allocation Of Medical Interventions In Outbreak Control:
The Case Of Ebola Virus
Farbod Farhadi, Roger Williams University, 1 Old Ferry Road,
Bristol, RI, 02809, United States,
The outbreak of Ebola in 2014 in western Africa is one of the fastest and deadliest
outbreaks in history of viral diseases, causing a reported 28 thousand suspected
cases and over 11 thousand deaths, according to WHO, leading to over 70%
fatality rate. Further outbreaks of the disease may occur in the future and fast and
effective containment strategies to control the spread is vital. In this study a
model for efficient allocation of medical interventions for outbreak containment is
presented.
5 - Cyclic Physician Scheduling Using Goal Programming
Hamoud Sultan Bin Obaid, PhD Student, University of Oklahoma,
1027 E Brooks St., Apt E, Norman, OK, 73071, United States,
, Theodore B Trafalis
A two-phase approach to construct a three-month schedule for physicians at an
outpatient clinic is proposed. The goal of the proposed model is to minimize the
variability of clinic and surgery sessions over the three-month period and utilize
resources. From mathematical point of view, the goal is to reduce the complexity
of solving this problem. The data used in this problem is obtained from King
Khaled Eye Specialist Hospital in Saudi Arabia.
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GIbson Board Room-Omni
Manufacturing III
Contributed Session
Chair: Zahra Sedighi Maman, Auburn University, Auburn, AL, 36849,
United States,
1 - Minimum Cost Allocation Of Quality Improvement Targets:
The Effects Of Forgetting And Knowledge Decay
Didun Peng, Purdue University, 610 Purdue Mall,
West Lafayette, IN, 47906, United States,
Weijia Wang, Robert Plante, Jen Tang
This paper incorporates knowledge depreciation in two dimensions of learning:
forgetting in autonomous learning and induced learning. We first present a
comprehensive quality cost progress function to account for both learning and
forgetting effects, where the forgetting effects are imbedded in the progress
function components of accumulated production and induced learning. Within
the context that a manufacturer allocates quality improvement targets to its
suppliers, an optimization model is developed to allocate induced learning
activities that minimize the total system cost. A numerical examples of an internal
supplier process is used to demonstrate the model.
2 - Sustainability and Changeability In Manufacturing System
Shima Ghanei, University of Minnesota -Duluth, 105 Voss Kovach
Hall,1305 Ordean Court, Duluth, MN, 55812, United States,
, Tarek Al Geddawy
Changeable Manufacturing Systems (CMS) are designed to quickly adapt to
changing market requirements by transition from a configuration to the next. Not
only is the reconfiguration cost dependent on degree of system convertibility and
scalability, but also dependent on what time of the year during which it is
performed, since energy pricing changes within and between seasons. This paper
presents a new linear mixed integer mathematical model to maximize
sustainability of CMS on the tactical level. It is solved by CPLEX solver in GAMS
software to analyze influence of volatile energy pricing and variable demand on
system convertibility and scalability which can affect layout configuration
selection.
3 - Printing The Future: Using Analytics To Advance
Additive Manufacturing
Sarah Powers, Oak Ridge National Laboratory, One Bethel Valley
Rd., P.O. Box 2008, Oak Ridge, TN, 37831, United States,
Recent advances in additive manufacturing have led to many success stories of
large 3D printed objects and leave the industry poised for rapid growth. This work
describes a multi-pronged approach for data discovery, engaging multiple analytic
tools as well as a framework to ingest and house the data itself in an effort to
identify areas for process improvement and promote the potential for advanced
defect detection.
4 - A Short Note On The Effect Of Sample Size On The Estimation
Error In Cp
Zahra Sedighi Maman, Auburn University, Auburn, AL, 36849,
United States, , William Murphy, Saeed
Maghsoodloo, Fatemeh HajiAhmadi, Fadel Megahed
Process Capability indices such as Cp are used extensively in manufacturing
industries. In practice the parameter for calculating Cp is rarely known and is
frequently replaced with estimates from an in-control reference sample. This
study explores the optimal sample size required, with some practical tools to
achieve a desired error of estimation using absolute percentage error of different
Cp estimates.
Sunday, 3:10PM - 4:00PM
Keynote
Davidson Ballroom A-MCC
Optimizing the Kiel Canal – Online Routing of
Bidirectional Traffic
Keynote Session
Michael Trick, IFORS President, Carnegie Mellon University, Pittsburgh,
PA 15213-3890,
1 - Optimizing The Kiel Canal – Online Routing Of Bidirectional Traffic
Rolf H Mohring, Beijing Institute of Scientific and Engineering
Computing, Beijing, China,
We introduce, discuss, and solve a hard practical optimization problem that deals
with routing bidirectional traffic on the Kiel Canal, which is the world’s busiest
artificial waterway with more passages than the Panama and Suez Canal together.
The problem arises from scarce resources (sidings) that are the only locations
where large ships can pass each other in opposing directions. This requires
decisions on who should wait for whom (scheduling), in which siding to wait
(packing) and when and how far to steer a ship between sidings (routing), and all
this for online arriving ships at both sides of the canal. The lecture is based on
joint work with Elisabeth Lübbecke and Marco Lübbecke.
Keynote
Davidson Ballroom B-MCC
Creating the Exascale Ecosystem for Science
Invited: Plenary, Keynote
Invited Session
Bogdan Bichescu, The University of Tennessee, Knoxville, TN
37996-0525,
1 - Creating The Exascale Ecosystem For Science
Jeff Nichols, Oakridge National Laboratory, Oak Ridge, TN, United
States,
The way we tackle grand challenge science questions at the national scale has
changed over the past two decades with the advent of both modeling and
simulation (M&S) and “big data” becoming more recognized and supported
discovery paradigms. In fact, most large scientific problems today are solved as
integrated solutions of experiment, theory, M&S, and data analytics. The past
several decades of high performance computing have focused on delivering
compute intensive systems and their performance measured by how fast they can
accomplish a simple matrix multiply (e.g., high performance linpack). Today’s
complex workflows require not only compute intensive capabilities, but also
capabilities that target data analytics approaches such as deep learning, graph
analytics, or map reduce. In this talk I will describe several scientific areas that
require an integrated approach and discuss the ecosystem [ORNL’s Leadership
Computing Facility (OLCF) and its Compute and Data Environment for Science
(CADES)] that we have created. We continue to invest in the evolution of this
ecosystem to enable successful delivery of important scientific solutions across a
broad range of disciplines.
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