2007-2008 Annual Report
Industrial Engineering
Arizona State University
IE@ASU
Research at a Glance
Production
Systems
& Logistics
Operations
Research
Industrial
Statistics
Information
&
Management
Systems
Contents
Faculty Honors & Awards
2
Sponsored Research
4
Ph.D. Degrees Advised
5
Research Features
6-13
Faculty Profiles 14
Regents’ Professor
14
Professors
15
Associate Professors
21
Assistant Professors
26
Visiting Professors
30
2007 Publications 31
* ASU IE Faculty Ranked 4th in
scholarly productivity by
The Chronicle of Higher Education
(details on page 3)
Industrial Engineering
2007-2008 Annual Report
W
elcome to our 2007-2008
Annual Research Report. The
report describes our goals,
accomplishments, and transitions over the
past year. We have spent considerable time
on strategic planning over the past year and
believe we have laid out a path that will further
our mission of enhancing the global quality of
life through leading discovery and innovation.
One notable achievement already recognized
is the announcement by The Chronicle of Higher
Education that our IE department ranks 4th
nationally for faculty scholarly productivity.
This ranking was based on a quantitative analysis
of the number and impact of faculty publications
and research grants. That’s quite an honor and a
tribute to the quality of our faculty.
deployment,
support, and
retirement
of
large
scale systems of systems, and, our active and
growing participation in health care delivery,
financial engineering, logistics and other service
industries. “Operations” acknowledges that
many IE’s are engaged in designing procedures
for and managing production operations
throughout a broad spectrum of manufacturing
and service industries and frequently use tools
associated with “operations research”.
The remainder of this report will detail
the specifics of our faculty and student
accomplishments. We encourage you to invest a
few minutes to learn what we are doing and invite
you to keep in mind that we are always interested
in partnering with industrial, government, and
other academic institutions to leverage our
talents through collaborative research. We’ll be
here, so if you see an opportunity where both
groups and the greater society can all benefit,
please contact us and let’s make it happen.
In keeping with our new strategic vision, we are
changing our name to Department of Industrial,
Systems and Operations Engineering. This new
name both better reflects where we are as a
department and where we are headed. “Systems”
recognizes our emerging activity in Systems
Engineering to support the needs of the regional
aerospace and defense industries that engage in
the “Spark to Dark” life-cycle of development, −Dr. Ronald G. Askin, Chair
Degrees Awarded 2007-2008
Enrollment Fall 2007
Bachelors
Masters
Doctoral
Bachelors
Masters
Doctoral
46
51
13
153
95
79
Faculty News, Honors & Awards
Montgomery recieves
international engineering award
Douglas Montgomery, a professor in the Department
of Industrial Engineering, has been selected to
receive one of the top honors bestowed by the
European Network for Business and Industrial
Statistics (ENBIS).
Montgomery will receive the 2008 George Box
Medal, recognizing outstanding contributions to the
development and application of statistical methods
in European business
and industry.
He will give an address
and be presented the
award at the ENBIS
international meeting
in Athens, Greece in
September.
The ENBIS awards
committee
cited
M o n t g o m e r y ’s
industrial
statistics
work in the design of
experiments, quality
Dr. Douglas Montgomery
control, applications of
linear models, and timeseries modeling and forecasting.
The committee also noted his authorship of several
books in the field and many journal articles that
reflected the depth of his expertise.
Montgomery has worked in engineering assignments
with major businesses such as Union Carbide
Corporation and Eli Lily and Company, and been
a consultant to many national and international
engineering organizations.
He has lectured extensively throughout the Americas,
Europe and the Far East, and is one of the co-editors
2
of Statistical Practice in Business and Industry, which
the ENBIS awards committee deems a “famous” book
in the statistics field.
In 2006, Montgomery was made an Arizona State
University Regents’ Professor. The designation is given
to faculty members at Arizona’s public universities
who have demonstrated exceptional scholarship and
outstanding achievement. He is one of six Ira A. Fulton
School of Engineering faculty members to hold the
Regents’ Professor designation.
Writer: Joe Kullman
Esma Gel receives top industrial
engineering award
Esma Gel, associate professor
in the Department of Industrial
Engineering, received the Hamid K.
Eldin Outstanding Young Industrial
Engineer in Education Award from
the Institute of Industrial Engineers
(IIE) at its annual conference in May
in Vancouver, Canada. The award
recognizes young IIE members
who have demonstrated leadership
and professionalism in industrial
engineering education.
Dr. Esma S. Gel
Since joining the Ira A. Fulton School of Engineering in
2000, Gel has been teaching graduate and undergraduate
courses in operations research and production systems.
Her research focuses on the use of applied probability
techniques for modeling, design and control of
production systems and supply chains, with emphasis
on workforce engineering. Her work has been published
in leading journals and funded by the National Science
Foundation and industrial partners such as Intel, IBM
and Infineon. Gel earned her masters of science and
Ph.D. degrees from Northwestern University in 1995
and 1999, respectively.
Arizona State University Industrial Engineering
honors
George Runger awarded for IIE
top applied IE research paper &
shared ASU President’s Medal
George Runger was awarded the 2008 IIE Transactions
Quality & Reliability Engineering BestApplication Paper
award; Runger is one of the authors of “Multivariate
Statistical Process Control with Artificial Contrasts,”
published in IIE Transactions: Special Issue on Data Mining
in 2007.
In addition to his best paper award, Runger was a
contributor to Arizona HealthQuery: A CommunityUniversity Partnership project, recognized with the
President’s Medal for Social Embeddedness. For the
project, he collaborated with other researchers in
biomedical informatics, and used applied statistical and
analytical modeling to correlate variables consistent
with different disease incidents.
Teaching accolades
Each year, the Ira A. Fulton School of Engineering recognizes its top teaching faculty. Three of the industrial
engineering faculty– Linda Chattin, lecturer, and professors George Runger and Dan Shunk–were recognized in the top 5% of best teachers in the school.
Study ranks IE@ASU No. 4
The ASU Department of Industrial Engineering ranks
4th in faculty scholarly productivity among U.S.
industrial engineering programs, according to The
Chronicle of Higher Education. The Chronicle study involved
creating a quantitative index of faculty scholarship and
research activity for all departments that offer a Ph.D.
degree.
The 2007 index measured faculty members for the
following five categories: books published, journal
publications, citations of journal articles, federal grant
dollars awarded, and honors and awards.
Villalobos featured in top 100
J. René Villalobos was featured in Revista Poder y Dinero,
one of the most prestigious financial magazines in Mexico and Latin America, in a special edition (October
2007) of the 100 professors who were born in Mexico
and are now teaching and “making waves” in the United
States. Selection was competitive, including review of
230 professors with highly significant accomplishments
and from universities such as Harvard and Columbia.
Top faculty are selected in a process that starts by
collecting student nominations, which are then
evaluated by the Quality of Instruction Committee. The top professors are then ranked according
to the influence they had on their students. Industrial engineering faculty are regularly recognized
as excelling in teaching evaluations.
Left to right:
Dr. George Runger,
Dr. J. René Villalobos
Faculty Honors & Awards 2007-2008
3
2007-2008 Sponsored Research
Project Title, Sponsor, P.I.(s)
Production Systems & Logistics
“Banner Throughput Collaborative: Operations
Research,” Banner Health, Jeffery Cochran
“Customer Relationship Management at Tyco
Electronics,” Tyco Electronics, John Fowler
“Factory Capacity Allocation Solver for Rapid
within Shift Re-Planning,” Intel, John Fowler
“Banner Health/ASU Partnership for ED Patient
Safety,” Banner Health, Jeffery Cochran
“Mayo Clinic Center for Clinical and Translational
Research,” Mayo Foundation, John Fowler
“ASAP Customization,” Advanced Micro Devices,
John Fowler, Gerald Mackulak
“EPNES: Integrated MEMS & Advanced
Technologies for the Next Generation Power
Distribution System,” NSF, Esma Gel
“The SRC Fellowship,” SRC, John Fowler, Gerald
Mackulak
“Scheduling Assembly and Test Facilities,” Intel,
John Fowler, Ron Askin
“GOALI Collaborative Research: Matching
Demand and Supply through Price and Lead
Time Decisions,” NSF-ENG Civil, Mechanical and
Manufacturing Innovation (CMMI), Esma Gel
“Improving Airline Schedule Planning at Swift
Aviation Group,” Swift Aviation Group, Ahmet Keha
“Pricing & Profit Optimization for Financial
Services,” Response Analytics, Teresa Wu
Information & Management Systems
“Collaborative Research: Hierarchical Modeling
of Yield & Defectivity to Improve Factory
Operations,” SRC, Douglas Montgomery
“Collaborative Research: Monitoring Process
& Product Quality Profiles,” NSF, Douglas
Montgomery
“SRP-PSERC Project 1997-2007,” SRP, Douglas
Montgomery
“Modeling & Analysis of Profiled Reliability Tests
Using Computation-Intensive Statistical Methods,”
NSF, Rong Pan
“Collaborative Research: Blind Discovery
of Variation Sources for Visualization by
Multidisciplinary Teams,” NSF, George Runger
“Self-Learning of Decision Rules for Process
Control,” NSF, George Runger
“Multi-Product Cycle Time & Throughput
Evaluation,” SRC, Gerald Mackulak, John Fowler
“Distributed Decision Support Framework for
Adaptive Supply Chains,” IBM, John Fowler, Teresa
Wu
“US-Mexico Partnership on Education &
Technology Transfer for the Aerospace Industry,”
USAID, Rene Villalobos, John Fowler, Esma Gel
“CAREER: Design & Implementation of a Virtual
Product Development Environment,” NSF, Teresa
Wu
“Data Mining Pilot on Intel Factory Data,” Intel,
George Runger
“Arizona State University Affiliation with the
Center for Engineering Logistics & Distribution
(CELDi),” NSF, Rene Villalobos, Ron Askin, Esma
Gel
“Fabrication Environmentally Conscious (Benign)
Manufacturing into Engineering Education,”
UTEP, Teresa Wu, Rong Pan
“Integration of Health Outcomes Information−A
Partnership with Arizona Department of
Environmental Quality,” ADEQ, George Runger
“Models of Quality of Service and Quality of
Information Assurance Towards Their Dynamic
Adaptation,” DOD-Air Force Research Labs, Nong Ye
“Feature Selection with Ensembles for Complex
Systems,” NSF, George Runger
“SoD: Design of Service-Based Software Systems
with QoS Monitoring and Adaptation,” NSF, Nong
Ye
Engineering Education
“Intelligent Food Defense Systems for International
Supply Chains: The Case of Mexico Fresh Produce
to the U.S.,” Department of Homeland Security, Rene
Villalobos, George Runger
“CELDi Membership: Forecast and Capacity
Planning for Nogales’ Ports of Entry (Nogales
POEs Traffic Study),” ADOT Research Center, Rene
Villalobos
Operations Research
“Predicting and Prescribing Human Decision
Making Under Uncertain and Complex Scenarios,”
AFOSR, Ronald Askin
“Multi-Product Cycle Time & Throughput
Evaluation via Simulation on Demand,” SRC, John
Fowler, Teresa Wu
“Collaborative Research: Developing and
Engineering Virtual Organization for DiscreteEvent logistics Systems,” NSF, John Fowler, Teresa
Wu
“Collaborative Research: Optimization of the
Design & Operation Surgery Delivery Systems,”
NSF, John Fowler
4
Industrial Statistics
“Regression-Based Quality Improvement in
Complex Systems with Consideration of Data
Uncertainty,” NSF, Jing Li
“Advanced Techniques in Design of Experiments
for Computational and Physical Multivariate
Experiments,” NASA, Douglas Montgomery
“Economical Concrete Mix Designs Utilizing
Blended
Cements,
Performance-Based
Specifications, and Rational Pay Factors,” ADOT
Research Center, Douglas Montgomery, Connie
Borror
“An Interdepartmental Computing Environment
for Statistical Research,” NSF, Douglas
Montgomery, George Runger, Connie Borror, and
faculty across ASU campus
Arizona State University Industrial Engineering
“Credit Risk Analytics,” Desert Schools Fed Credit
Union, George Runger
“Collaborative
Interdisciplinary
Research
Community (CIRC),” NSF, Mary AndersonRowland
“Collaborative Research: Maricopa Engineering
Transition Scholars (METS),” NSF, Mary
Anderson-Rowland
“Collaborative
Interdisciplinary
Research
Community Maricopa Engineering Transition
Scholars (CIRC/METS),” NSF, Mary AndersonRowland
“NACME Scholars Program,” NACME, Mary
Anderson-Rowland
“Academic & Professional Development for
Upper-Division Computer Science, Engineering
and Mathematics Students,” NSF, Mary AndersonRowland
“Academic & Professional Development for
Lower-Division Computer Science, Engineering
and Mathematics Students,” NSF, Mary AndersonRowland
2007-2008 Ph.D. Degrees Granted
Summer 2007
Jing Hu
Change Detection with Supervised Learning
Advisor: George Runger
Placement: SRP
Arife B. Colak
Hybrid Algorithms for Combinatorial Optimization Problems
Advisor: Ahmet Keha
Placement: Central New Mexico Community College
Fall 2007
Hugo C. Garcia
A Framework for the Self Reconfiguration of AutomatedVisual
Inspection Systems
Advisor: René Villalobos
Placement: Freescale Semiconductor
Napatkamon Ayutyanont
Statistical Characteristics and Models of Cyber Attack and Norm
Data for Cyber Attack Detection
Advisor: Nong Ye, Randall Eubank
Placement: Banner Health Alzheimer Institute
Sandipan Ganguly
Compromise Based Design: A Penalty Function Approach to
Distributed and Collaborative Optimization in Design
Advisor: Teresa Wu, Ahmet Keha
Placement: Expedia.com
Darshit B. Parmar
Mitigating Supply Chain Disruption Risk Using Sense and Respond
Framework
Advisor: Philip Wolfe, Teresa Wu
Placement: IBM
Eric C. Maass
Modeling the on Time Delivery and Inventory for Semiconductor
Supply Chains
Advisor: John Fowler, Murat Kulahci
Placement: Motorola
Myrta R. Sigufuentes
Evaluation and Construction of Optimal Experimental Designs for
Fitting Response Surface Models
Advisor: Douglas Montgomery, Connie Borror
Placement: Tecnológico de Monterrey, Hermosillo Campus
Yang Sun
Strategic and Operational Product Allocation in Semiconductor
Supply Chains
Advisor: Dan Shunk, John Fowler
Placement: California State University, Sacramento
Spring 2008
Jennifer M. Bekki
Cycle-Time Quantile Estimation with Discrete Event Simulation
Advisor: John Fowler, Gerald Mackulak
Placement: Arizona State University, Polytechnic campus
Ozgun B. Bekki
Dynamic Price and Lead Time Quotation Strategies
Advisor: Esma Gel
Placement: independent contractor
Michael Chiaramonte
Competitive Nurse Rostering and Rerostering
Advisor: Jeffrey Cochran, Teresa Wu
Placement: U.S. Air Force in Japan
Hugo Garcia, Ph.D., at Spring 2008 graduation.
Ph.D. Degrees Advised
5
Research contributors to Arizona HealthQuery : A Community-University Partnership
project, recognized with the President’s Medal for Social Embeddedness.
BioInformatics
Methodologies
We discovered
statistically
significant
relationships
between
air quality
and asthma
incidents in
children.
6
B
ioinformatics uses and develops
techniques from disciplines such
as statistics, machine learning,
and data mining, to find solutions to biological problems. Large amounts of data
are collected on public health, environmental, and genomic information. When
that data is analyzed with modern analytical methods, the new knowledge can
point to causes and aid in prevention or
treatment. To solve such problems in bioinformatics, Dr. George Runger is teaming up with researchers across Arizona
State University and the greater Arizona
community. Applications of data mining
and statistics in bioinformatics are already showing promising results that will
improve public health.
In one such project, the group is developing
new methods to monitor public health
data on occurrences of staph infections
to detect changes in the community
health
status. Methicillin-resistant
staphylococcus aureus (MRSA) is a strain
of bacteria that is resistant to broadspectrum antibiotics. Saylisse Davila, an
industrial engineering graduate student
researcher working with Dr. Runger, said
that by “leveraging substantial experience
with multidimensional monitoring of
large industrial data sets, the plan is to
conduct analyses spatially, temporally,
and with additional covariates (such as
demographics, service provider, etc.)
with sensitivity to changes that occur only
for local regions and/or subpopulations.”
Arizona State University Industrial Engineering
Their hope is to show relationships
in the analysis that will point to
methods of reducing infection.
Linking data is also showing
correlations between local instances
of asthma and air quality. Dr.
Runger is a researcher involved
in the children’s health project, a
collaborative partnership among
the United States Environmental
Protection Agency (USEPA),Arizona
Department of Environmental
Quality
(ADEQ),
Arizona
Department of Health Services
(ADHS), the ASU Center for Health
Information and Research (CHIR),
ASU Mechanical and Aerospace
Engineering, and ASU Industrial
Engineering. The study’s goal is
to first, “explain the relationship
between asthma in children and air
quality particulates and then develop
an enhanced warning system.”
researcher on the project, said, “We
discovered statistically significant
relationships between air quality and
asthma incidents in children.”
Researchers said that the project
provided an example of a complex
analysis with a large team to relate
health effects with environmental
factors.
“The
project
also
demonstrated the capability for
various organizations to collaborate
and link data, plan studies, cooperate
for analysis, and communicate
findings. We disseminated to other
stakeholders, such as the asthma
coalition, the University of Arizona
medical school, and CHIR data
partners.” The work also led to
development of new bioinformatics
tools for these types of analyses.
gene expression and cell state.
The rich data set will provide
multiple empirical distributions
of oxygen measurements for
several time intervals. New feature
extraction algorithms are planned
to
characterize these empirical
distributions to relate oxygen
consumption rates to gene expression
and cell condition. Furthermore, the
time element of the data potentially
enables one to enhance the feature
extraction methods with the
temporal patterns as well.
This is a unique data set with
supervised
information
and
temporal components that requires
dimensionality reduction. Even
for a large starting set of candidate
features, it is computationally fast to
select the most predictive of mRNA
New bioinformatics tools are also abundance or cell condition that
an aid in genomic research. The could lead to relevant information
research team from the Department to understand diseases. These
Extensive health data from CHIR of Industrial Engineering, is capabilities will be used to detect
and ADHS provided information collaborating with the Center for features important either individually
on thousands of asthma incidents. Ecogenomics, Biodesign Institute or involved in interactions.
This data needed to be linked to at Arizona State University to help
environmental air quality data from investigate the inherent variety of Doctoral
student
researcher,
multiple sensors with important cells and relationships with diseases Wandaliz Torres-Garcia, explained
spatial and temporal components. such as cancer. One research endeavor that, “Despite the increasing
Because asthma has a strong seasonal in the center is the development of molecular knowledge and the
component, the case-crossover single-cell imaging technologies to technological advances to gather
method and other analytical tools measure oxygen consumption rates, data from biological processes, there
to control for long term trends, because of the strong correlation remain areas for innovation in data
seasonal effects, epidemics, and with cell function.
analysis to achieve suitable biological
other covariates that change slowly
interpretations. Cell pathways
with time were used. Nuttha New bioinformatics tools are being are still unclear for many diseases
Lurponglukana,
an
industrial developed to study the relationships and understanding is critical for
engineering
doctoral
student between oxygen consumption rate, successful treatments.”
Research: Industrial Statistics
7
Robust
Optimization
The advantage
of [using robust
optimization]
is that the
problem size
will not increase
dramatically...
and will achieve
a high quality
decision.
O
ptimization has a history that goes back to World War II and
has made significant contributions to important, real-world
problems faced by organizations in government and private
industry. Blending optimization with modeling, engineers create airline
flight schedules, production plans, and even urban planning designs that
maximize customer satisfaction while using only the available resources and
satisfying organizational and technological constraints. Even so, the world is
unpredictable. Historically, the development of optimization has assumed the
world to be known; in reality, we encounter unexpected events every day.
Solutions are needed that provide good results for all possible futures. Robust
optimization is a new paradigm shift to address this issue for large problems.
“Uncertainty is one of the important issues to consider when people make
decisions, “says Dr. Muhong Zhang, assistant professor in the Operations
Engineering group. “Robust optimization approach is one of the methodologies
to handle this aspect.” Simple approaches, such as just planning for the
expected or most likely scenario, can lead to very bad decisions under some
possible events.
“Unlike traditional stochastic programming, robust optimization does not
assume the probability distributions of the uncertain parameters. Instead, we
8
Arizona State University Industrial Engineering
Dr. Muhong Zhang
general network problem with
uncertain parameters. One goal is
to apply such techniques to practical
problems, for example, production
planning in the semiconductor
industry. Second, in such network
problems, there are problems
that can be solved efficiently. I am
characterizing such problems and
developing efficient algorithms for
them.”
of the completed products to the
retailers. Using the algorithms she
develops will hopefully decrease
computational time and offer
decision makers the supporting
information they need to make
critical decisions.
Dr. Zhang
is looking to apply her robust
optimization methodology to other
real-world problems that can be
modeled as flow across a network.
She is now studying the two-stage,
robust network flow and design
problem with demand uncertainty,
when applied to supply chain and
logistics problems. In her research,
robust optimization happens in two
stages, which allows a company to
go ahead and schedule one stage
of production, even without a full
picture of what the eventual demand
will be. In the second stage, after
decision makers observe actual
outcomes, updated information
“Currently, I am working on the can be used to schedule shipment
Muhong Zhang joined the
Operations Research (OR) group
after completing her Ph.D. at
the University of California,
Berkeley, and serving as a
lecturer there for one year. She
previously earned a Master’s
degree in Operations Research
from the Applied Mathematics
Institute, Chinese Academy of
Sciences; and a Bachelor’s degree
in Applied Mathematics from
Beijing University of Chemical
Technology.
consider a range of possible values
that the uncertain parameters
can take. The advantage of such
approach is that the problem size
will not increase dramatically,
compared to traditional stochastic
programming, to achieve a highquality decision. However, the
reformulated problem, what is
called the robust counterpart, may
not be an easy problem, either. “
Dr. Muhong Zhang chose to
research solutions to such
problems with random variables
because of a broadened view of
robust optimization in recent
years. Zhang’s past and present
research has been on developing
techniques in robust optimization,
transportation, and distribution
in
logistics,
mixed-integer
programming,
combinatorial
optimization, and network flows.
Research: Operations Research
9
Dr. John Fowler
Scheduling
from semiconductors to hospitals
D
r. John Fowler and his colleagues and
students have been involved in using
deterministic scheduling methodology
for a wide range of applications.The applications
are as diverse as scheduling semiconductor
manufacturing operations and surgical delivery
systems (aka operating rooms).
Scheduling involves making decisions about the
allocation of limited resources to operations
over time. These decisions play a crucial role in
determining the competitiveness (and in some
cases the survivability) of manufacturing or
service enterprises. Manufacturing companies
have to meet shipping dates to their customers, as
a failure to do so would result in a significant loss
of good will. Service companies must provide
their services in reasonable time or customers
will find other service providers. Both must
schedule their operations in order to effectively
utilize expensive resources (e.g. machines or
surgical rooms). Scheduling problems are
technically quite challenging. The difficulties
encountered are similar to the difficulties
encountered in other branches of combinatorial
10
optimization and stochastic modeling. Even
for problems that seem quite simple, the time
required to determine the optimal solution can
be very long, unless special structure in the
problem can be found and exploited.
Dr. Fowler joined the Industrial Engineering
(IE) department at ASU in 1995 after spending
5 years at SEMATECH, an R&D consortium of
semiconductor manufacturers. In 1997, he was
awarded a 3-year grant, jointly funded from
the National Science Foundation (NSF) and the
Semiconductor Research Corporation (SRC),
entitled “Wafer Fab Operations: Modeling,
Analysis and Design”. He and his research
colleagues from MIT and the University
of Illinois focused on the development of
operational modeling tools and techniques
(including scheduling) to improve the efficiency
of wafer fabrication. As part of this effort, one
of Dr. Fowler’s Ph.D. students, Scott Mason
(now an Associate Professor at the University of
Arkansas), developed a shifting bottleneck-based
approach to scheduling wafer fab operations.
Following that project, Dr. Fowler led a team
Arizona State University Industrial Engineering
of researchers, including ASU colleagues Professors
George Runger and Esma Gel, Professor Mason,
and three colleagues from German institutions, on a
proposal to the Factory Operations Research Center,
funded by SRC and International Sematech, entitled
“Scheduling of Semiconductor Wafer Fabrication
Facilities.” Over two years, the PIs worked together
effectively to:
More recently, Dr. Fowler has turned his attention
to scheduling surgical services. Surgical services
require the coordination of many activities, including
patient check-in and pre-procedure preparation,
the surgical procedure, and recovery. ASU IE Ph.D.
student, Serhat Gul, and Dr. Fowler teamed up with
Todd Huschka and Dr. Brian Denton from the Mayo
Clinic in Rochester, MN, to develop a simulation
model of an outpatient procedure center (OPC).
• Develop viable shifting bottleneck-based wafer fab Through the use of the model, they demonstrated
scheduling and rescheduling methodologies;
that how surgeries are scheduled has an impact on the
• Develop and test wafer fab-specific subproblem competing objectives of mean patient waiting time
solution procedures for parallel machines requiring and the amount of overtime of the OPC. In particular,
auxiliary resources (steppers needing reticles), batch they found that arrival time schedules substantially
processing machines (diffusion ovens), and machines influence expected overtime and patient waiting time,
characterized by sequence-dependent setups while surgery allocation and sequencing heuristics
(implanters);
have a smaller effect. Furthermore, they found that
• Investigate the utility of statistical operations control surgery mix on a particular day is an important factor
in determining appropriate rescheduling “triggers,” affecting performance measures, indicating that the
such as deviation from expected job completion optimization of daily surgical mix may be a promising
time; and
opportunity for improving scheduling efficiency in an
• Create an AutoSched AP-based testing environment OPC. In addition, the model developed for the OPC
to evaluate scheduling approaches in a dynamic, has become the starting point for a model that is
simulation-based environment in order to being used to help design a new outpatient procedure
accommodate real-world fab models.
center. In the continuation of their National Science
Foundation projects (DMI-0620573 (Denton) and
Experimental results demonstrated the efficacy DMI-0620504 (Fowler)), they will continue to use
of scheduling wafer fabs to maximize delivery the model to study how to improve OPC operations.
performance of customer orders in an acceptable
amount of computation time. The goal of the In addition to the project described above, ASU IE
experimentation was to find scheduler parameters Ph.D. student Qing Li and Dr. Fowler have worked
that maximize on-time delivery performance of with colleagues at ASU to improve the capacity
orders from customers of varying importance/ planning and day-to-day scheduling of patients
priority (i.e., total weighted tardiness or TWT) for cardiac catheterization procedures. Based on
while running at a bottleneck utilization of 95%. an in-depth study of a major heathcare facility in
The TWT results of the scheduler were compared to Arizona, they developed a good understanding of
those obtained using classical dispatching approaches the scheduling problem. By block-scheduling, an
like: first in, first out (FIFO); earliest due date initial schedule is generated and then adjusted by a
(EDD); apparent tardiness cost with setups (ATCS); real-time scheduling algorithm. The decision makers
critical ratio (CR); and operational due date (ODD). can trade-off between multi-objectives and make
The new scheduler TWT results were between 1% decisions depending on the importance of different
and 25% of the corresponding best dispatching objectives in the healthcare, i.e. patient waiting,
results. In summary, the results demonstrated that a facility utilization and staff overtime. The approach
deterministic scheduling-based wafer fab scheduling was shown via simulation to improve the performance
system has the potential to improve the on time of the scheduling in all measurements. The approach
delivery performance of wafer fabs without loss of has been implemented in the healthcare facility and
throughput. This research has motivated several shown improvements in the pilot study.
commercial software companies to begin to develop
deterministic scheduling systems.
Research: Production Systems & Logistics
11
Design
Collaboration
C
reated to improve processes, industrial
engineering is especially relevant
for today’s companies to address the
“dynamic, globalized and customer-driven
markets” in which they do business, says Dr.Teresa
Wu, associate professor in the Information and
Management Systems group of the Department
of Industrial Engineering. Expectations from a
demanding global customer drives the search
for technology and strategies that will help meet
cost, quality and delivery goals. One strategy of
modern enterprises is called Collaborative Product
Development (CPD), where partnerships are
developed in an effort to improve product quality,
and reduce manufacturing costs and production
time. “CPD is pressing hard to…form a virtual
enterprise, in which partners collaboratively
respond to the changes of customer demand in a
swift manner,” says Dr. Wu.
An important component of a successful CPD
is reliable communication through a web-based,
collaborative information system, which the
researchers say “ensures the right information is
quickly provided to the right place, at the right
time, in the right format.” Another aspect of
CPD is the effective decision support system,
which helps collaborating engineers develop
products quickly and cost-effectively. As the
Internet facilitates the changing of information
management systems from traditional,
centralized systems to distributed systems, it
12
Arizona State University Industrial Engineering
Dr.TeresaWu
enlarges the set of potential collaborators
and increases the dynamics of a partner’s
relationship—bringing about opportunities and
threats.
While extensive research has addressed
methodologies and Internet applications to
CPD, some challenging questions remain
unanswered. Dr. Wu and her collaborators are
asking: “What foundation of understanding is
necessary for collaborating engineers to design
and develop a world-class product? In what
framework can engineers across the globe
actively participate and proactively develop
world-class products?” Specifically, the research
aims to determine how a company should prequalify partners to perform the constituent
responsibilities of a business initiative,
including what is a suitable methodology to
analyze conflict among engineers and what is
an appropriate mechanism for engineers to
converge to an agreeable design.
The goal of her current research is to “design
and implement a Virtual Product Development
Environment (VPDE) to address these
questions.” The research team is exploring
the modular product development problems,
for example, the design of electro-mechanical
artifacts. The contributions of VPDE are
expected to: first, develop an Internet-based
engineering information system that can
handle both public and private information,
particularly the secured communication
of collaborators’ private information; and
second, to develop a distributed decision
support system, integrated with partner prequalification, including a dynamic analysis
that will help partners go from conflict
to negotiation to resolution. Researchers
expect that “VPDE will speed the product
development process, reduce cost and increase
productivity.”
Along with student researchers in her
Intelligent Decision Systems laboratory (IDS),
Dr. Wu is working on CPD with researchers
across the nation. Collaborators include Tom
Thurman and M.C. Jothi of Rockwell Collins;
James Andary of Nasa Goodard Space Flight
Center; Kemper Lewis of the University of
New York, Buffalo; and Zhouzi Zhao of GE.
Their research is “multidisciplinary, including
design optimization, decentralized decision
making, reliability-based design optimization,
and information technology used to facilitate
the communication among different disciplines.
So far, the project has mainly focused on
product design, yet, it has great potential
for system design. We believe this research
has great potential to be used to design
reconfigurable systems, such as healthcare and
urban systems.”
VPDE will speed the product
development process,
reduce cost and increase
productivity.
Research: Information & Management Systems
13
Regents’ Professor
Selected Publications
Chung, P.J., Goldfarb, H.B., and Montgomery, D.C.
“Optimal Designs for Mixture-Process Experiments
with Control and Noise Factors,” Journal of Quality
Technology, Vol. 39, No. 3, pp. 179-190, 2007.
Holcomb, D.R., Montgomery, D.C., and Carlyle,
W.M. “The use of Supersaturated Experiments in
Turbine Engine Development,” Quality Engineering,
Vol. 19, No. 1, pp. 17-27, 2007.
Douglas Montgomery
Regents’ Professor
Co-Director, Executive Committee on Statistics
Ph.D., 1969, Virginia Polytechnic Institute and State University
Statistical design of experiments, optimization and response surface
methodology, empirical stochastic modeling and industrial statistics
Quality and Reliability Engineering Laboratory (Q&RE lab)
D
ouglas Montgomery is Regents’ Professor of Industrial
Engineering and Statistics and the ASU Foundation Professor
of Engineering at Arizona State University. He received a
Ph.D. in engineering from Virginia Polytechnic Institute and State
University.
His research interests focus on designed experiments for product/
process design and development, empirical model-building, and
process monitoring and control. Dr. Montgomery is an author of 11
books that have appeared in over 30 English editions and numerous
foreign language editions and over 200 archival journal papers. He
has mentored 50 Ph.D. students and over 40 M.S. students. He is a
recipient of the Shewhart Medal, the Brumbaugh Award, the Lloyd S.
Nelson Award, the William G. Hunter Award, and the Shewell Award
(twice) from the American Society for Quality. He is also a recipient of
the Ellis R. Ott Award. He is a former editor of the Journal of Quality
Technology and is the currently one of the Chief Editors of Quality &
Reliability Engineering International. He serves on the editorial boards
of several other professional journals. Dr. Montgomery is a Fellow
of the American Statistical Association, a Fellow of the American
Society for Quality, a Fellow of the Royal Statistical Society, a Fellow
of the Institute of Industrial Engineers, an Elected Member of the
International Statistical Institute, and an Elected Academician of the
International Academy for Quality.
Jearkpaporn, D., Borror, C.M., Runger, G.C., and
Montgomery, D.C. “Process Monitoring for Mean
Shifts for Multiple Stage Processes,” International
Journal of Production Research, Vol. 45, No. 23, pp.
5547-5570, 2007.
Lawson, C. and Montgomery, D.C. “A Logistic
Regression Modeling Approach to Business
Opportunity Assessment,” International Journal of Six
Sigma and Competitive Advantage, Vol. 3, No. 2, pp.
120-136, 2007.
Perry, L.A., Montgomery, D.C., and Fowler, J.W.
“A Partition Experimental Design for a Sequential
Process with a Large Number of Variables,” Quality
and Reliability Engineering International, Vol. 23, No.
5, pp. 555-564, 2007.
Chatlani, V.P., Tylavsky, DJ., Montgomery, D.C.,
and Dyer, M. “Statistical Properties of Diversity
Factors for Probabilistic Loading of Distribution
Transformers,” 39th North American Power Symposium
(NAPS 2007), pp. 581-587.
Almimi, A.A., Kulahci, M., and Montgomery, D.C. “Follow-up Designs to Resolve Confounding in
Split-Plot Experiments,” Journal of QualityTechnology,
Vol. 40 (2), 2008, pp. 154-166.
Leadership Activities
Editor, Quality and Reliability Engineering International;
Editorial Advisor, Journal and Probability and Statistical
Science; Editorial Board, Quality Engineering; Editorial
Board, Total Quality Management; Editorial Board,
Journal of Quality Technology; Editorial Board, Journal
of Applied Statistics; Editorial Board, International
Journal of Production Research; Editorial Board,
International Journal of Six Sigma.
Regents’
14
Arizona State University Industrial Engineering
Professors
Ronald Askin
Professor and Chair
Ph.D., 1979, Georgia Institute of Technology
Design and operation of discrete manufacturing
systems, supply chain logistics, decision analysis, applied
operations research, facilities planning, industrial
statistics and applied optimization
R
onald G. Askin is a Professor and Department Chair
of Industrial Engineering at Arizona State University.
He has authored or co-authored over 80 professional
publications, primarily on the application of operations
research and statistical methods to the design and analysis
of production systems. His current research concentrates
on developing integrated models for operational planning
including facilities design, production planning, scheduling,
material flow, and quality assurance. Other research interests
include project management, team formation, and human
decision making. Dr. Askin co-authored the texts Modeling and
Analysis of Manufacturing Systems (1993) and Design and Analysis
of Lean Production Systems (2002), both of which received the
IIE Joint Publishers Book of the Year Award (1994 and 2003,
respectively).
Dr. Askin is a Fellow of the Institute of Industrial Engineers
(IIE), and an active member of the Institute for Operations
Research and Management Science (INFORMS) and the
Society of Manufacturing Engineers (SME). He is past Chair
of the Council of Fellows for IIE and currenty serves on the
IIE Board of Trustees.
Selected Publications
Askin, R.G., Fowler, J.W., Fu, M., and
Li, Q. “Optimal Shade Location for Urban
Environments,” Proceedings of the IE Research
Conference, Vancouver, CA, 2008, 6 pages.
Chen, J. and Askin, R. G. “Project Selection
and Scheduling with Time Dependent Payoffs,”
European Journal of Operational Research, in press,
2007. DOI 10.1016/j.ejor.2007.10.040
Askin, R.G., Pew, M., Pabst, D., and Son, Y.
“Using Real Time Information in Operational
Planning and Control,” Proceedings of the 19th
International Conference on Production Research,
Valparaiso, Chile, 2007, 6 pages.
Krishnan, S., and Askin, R.G. “Effect of
Information Sharing and Control Strategies
on Supply Chain Performance,” International
Journal of Simulation and Process Modeling, 3/4,
2006, pp. 175-187.
Askin, R. G. and Chen, J. “Dynamic Task
Assignment for Throughput Maximization with
Worksharing,” European Journal of Operational
Research, 168(3), 2006, pp. 853-869.
Askin, R.G. and Goldberg, J.B. Design and
Analysis of Lean Production Systems. John Wiley &
Sons, 2002.
Professors
Other awards he has received include the IIE Transactions on
Design and Manufacturing Best Paper Award (twice as coauthor), the Shingo Award for Excellence in Manufacturing
Research, IIE Transactions
Development and Applications
Award (co-author), the ASEE/IIE Eugene L. Grant Award
(co-author), and the National Science Foundation Presidential
Young Investigator Award.
Leadership Activities
Editorial Board, International Journal of
Industrial and Systems Engineering; Special
Issue Co-Editor, International Journal of
Production Economics; Board of Trustees,
Institute of Industrial Engineers.
Professors
15
John Fowler
Professor
Ph.D., 1990, Texas A&M University
Deterministic scheduling, discrete event simulation methodology, semiconductor manufacturing systems analysis, healthcare
systems analysis and applied operations research
Modeling And Analysis For Seminiconductor Manufacturing
Laboratory (MASM lab): ie.fulton.asu.edu/research/masm-lab
J
Selected Publications
Fowler, J.W.,Wirojanagud, P., and Gel, E.S. “Heuristics forWorkforce
Planning with Worker Differences,” European Journal of Operational
Research, Vol. 190, No. 3, pp. 724-740, 2008.
Pfund, M.E., Balasubramanian, H., Fowler, J.W., Mason, S.J., and
Rose, O. “A Multi-criteria Approach for Scheduling Semiconductor
Wafer Fabrication Facilities,” Journal of Scheduling, Vol. 11, No. 1, pp.
29-47, 2008.
Laub, J.D., Fowler, J.W., and Keha, A.B. “Minimizing Makespan
with Multiple Orders per Job in a Two Machine Flowshop,” European
Journal of Operational Research, Vol. 182, No. 1, pp. 63-79, 2007.
Stray, J., Fowler, J.W., Carlyle, W.M., and Rastogi, A.P. “EnterpriseWide Strategic and Logistics Planning for Semiconductor
Manufacturing,” IEEE Transactions on Semiconductor Manufacturing, Vol.
19, No. 2, pp. 259-268, 2006.
Fowler, J.W., Kim, B., Carlyle, W.M., Gel, E.S., and Horng, S.M.
“Evaluating A Posteriori Solution Techniques for Bi-Criteria Parallel
Machine Scheduling Problems,” Journal of Scheduling, Vol. 8, No. 1,
pp. 75-96, 2005.
Park, S., Fowler, J.W., Mackulak, G.T., Keats, J.B., and Carlyle, W.M.
“D-Optimal Sequential Experiments for Generating a SimulationBased Cycle Time-Throughput Curve,” Operations Research, Vol. 50,
No. 6, pp. 981-990, 2002.
ohn W. Fowler is a professor in the operations research and
production systems and logistics groups. Much of his research
has focused on scheduling and simulation methodologies for
application in semiconductor manufacturing. His research
has been well supported by the National Science Foundation
(NSF), the Semiconductor Research Corp., International
SEMATECH, as well as by several leading semiconductor
manufacturers. Over the last three years, he has begun research
on applications of scheduling, simulation, and other operations
research techniques to health care and was recently awarded a
grant from the National Science Foundation to investigate ways
to schedule surgical delivery systems. He has also been working
with the Mayo Clinic to develop “A Curriculum for the Science
of Healthcare Delivery Systems.”
Dr. Fowler has co-authored over 60 journal articles in outlets
including Computers and Operations Research, Decision Sciences,
European Journal of Operational Research, IIE Transactions, IEEE
Transactions on Semiconductor Manufacturing, Journal of Scheduling,
and Operations Research. In addition, he has co-authored 11 book
chapters and nearly 100 conference papers. He has advised
or co-advised 25 Ph.D. students, 22 Master’s students, and 3
undergraduate Honor’s students.
Dr. Fowler is a Fellow of the Institute of Industrial Engineers (IIE),
is a member of the Board of Directors of the Winter Simulation
Conference, is Treasurer of Omega Rho (the IE Honor Society),
and is the INFORMS Vice President for Chapters/Fore. He was
co-Program Chair of the 2008 Industrial Engineering Research
Conference and Program Chair for the 2008 Winter Simulation
Conference.
Leadership Activities
Area Editor–Manufacturing, SCS Transactions on Simulation; Area
Editor–Planning & Scheduling, Computers and Industrial Engineering;
Associate Editor, IEEE Transactions on Electronics Packaging Manufacturing;
Associate Editor–Factory Modeling and Control, IEEE Transactions on
Semiconductor Manufacturing;
16
Editorial Board, IIE Transactions; Editorial Board, Journal of the Chinese
Institute of Industrial Engineers; Editorial Board, Journal of Simulation;
Guest Editor–eManufacturing in the Semiconductor Industry, IEEE
Transactions on Automation Science and Engineering.
Arizona State University Industrial Engineering
Gary Hogg
Professor
Ph.D., 1972, University of Texas at Austin
Applied optimization, simulation, manufacturing planning
and control
G
ary L. Hogg is currently a Professor of Industrial
Engineering at Arizona State University. He holds
an M.S. and Ph.D. from the University of Texas
in Operations Research, and B.S.M.E. from Texas A&M
University.
His graduate training and subsequent research has been
in the area of applying operations research, particularly
simulation and optimization, to the design and control of
production and service systems. He has taught a broad range
of operations research and industrial engineering courses
during his 35-plus year academic career, published widely,
and conducted research for NSF, NASA, USAF, DOE, EPRI,
DOT, DOD and the DOC. He has also served as a consultant
to over twenty-five Fortune 500 corporations, but also many
smaller manufacturers.The bulk of his industrial experience
is in high tech manufacturing, particularly aerospace and
electronics.
Leadership Activities
Associate Editor–Probabalistic Models, Computers
& Industrial Engineering
He served as Program Head of IE, Interim Head of the
IE Department and Asscociate Dean for Research and
International Programs at Texas A&M. From 1995 through
2005 he served as the Chair of Industrial Engineering at
Arizona State University. He is a Fellow of the Institute
of Industrial Engineers and has served on the IIE Board of
Trustees, Chair of the Council of Industrial Engineering
Academic Department Heads, VP of Technical Societies,
Director of the OR Division and President of the Arizona
Chapter of IIE as well as the Brazos Valley Chapter (Texas).
Professional service includes as Editorial Consultant to the
National Research Council for Modeling and Simulation
in Manufacturing and Defense Systems Acquisition, 2002;
Contributing Editor for McGraw-Hill Yearbook of Science and
Technology, 2002 through 2008, and the Encyclopedia of Science
and Technology, 2005; and as Area Editor for the Journal of
Computers in Industrial Engineering from 2000 to 2007.
Professors
17
George Runger
Professor
Ph.D., 1982, University of Minnesota
Statistical learning, process control, and data
mining for massive, multivariate data sets with
applications in numerous disciplines
Quality and Reliability Engineering Laboratory
(Q&RE lab)
G
eorge C. Runger, Ph.D., is a Professor in
the department of Industrial Engineering
at Arizona State University. His research
is on real-time monitoring and control, data
mining, and other data-analysis methods with a
focus on large, complex, multivariate data streams.
His work is funded by grants from the National
Science Foundation and corporations. In addition
to academic work, he was a senior engineer at IBM.
He holds degrees in industrial engineering and
statistics.
Selected Publications
Hwang, W., Runger, G.C., and Tuv, E. “Multivariate
Statistical Process Control with Artificial Contrasts,” IIE
Transactions: Special Issue on Data Mining, 39(6), 2007, pp. 659669. IIE Transactions on Quality and Reliability Engineering Best
Application Paper Award 2007.
Jearkpaporn, D., Borror, C.M., Runger, G.C., and
Montgomery, D.C. “Process Monitoring for Mean Shifts for
Multiple Stage Processes,” International Journal of Production
Research, Vol. 45 (23), 2007, pp. 5547-5570.
Berrado, A. and Runger, G.C. “Using Metarules to Organize
and Group Discovered Association Rules,” Data Mining and
Knowledge Discovery, 14(3), 2007, pp. 409-431.
Runger, G.C., Barton, R.R., Castillo, E. Del, and Woodall,
W.H. “Optimal Monitoring of Multivariate Data for Fault
Patterns,” Journal of Quality Technology, Vol. 39, No. 2, pp.
159-162, 2007.
Runger, G.C., Barton, R.R., Del Castillo, E., Woodall, W.H.
“Optimal Monitoring of Multivariate Data for Fault Patterns,”
Journal of Quality Technology, 39(2), 2007, pp. 159-162.
Hu, J., Runger, G.C., and Tuv, E. “Contributors to a Signal
from an Artificial Contrast,” Informatics in Control, Automation
and Robotics II, pp. 71-78, Springer, Netherlands, 2007.
Hu, J., Runger, G.C., and Tuv, E. “Tuned Artificial Contrasts
to Detect Signals,” International Journal of Production Research:
Special Issue on Control Charts, Vol. 45 (23), 2007, pp.55275534.
Leadership Activities
Department Editor, Journal of Quality Technology;
Associate Editor, Journal of Mathematical and Management Sciences.
18
Arizona State University Industrial Engineering
Dan Shunk
Professor, AVNET Chair
Ph.D., 1976, Purdue University
Agile, enterprise and CIM systems, group
technology, planning systems, economics of
computer-integrated manufacturing (CIM),
strategy and strategic role of technology
Supply Network Integration Laboratory (SNIL)
Selected Publications
Duarte, B., Fowler, J.W., Knutson, K., Gel, E., and Shunk,
D. “A Compact Abstraction of Manufacturing Nodes in
a Supply Network,” International Journal of Simulation and
Process Modeling, Vol. 3, Nos. 3, 2007, pp. 115-126.
Shunk, D., Carter, J., Hovis, J., and Talwar, A. “Electronics
Industry Drivers of Intermediation and Disintermediation,”
International Journal of Physical Distribution and Logistics
Management, Volume 37, No. 3, 2007, pp. 248-261.
Shunk, D., Carter, J., Hovis, J., and Talwar, A. “The
Drivers of Intermediation and Disintermediation when
the Industry is Under Stress,” International Journal of Physical
Distribution and Logistics Management, Vol. 37, No. 3, 2007,
pp 248-261.
Wu, T., Blackhurst, J., Shunk, D., Appalla, R. “AIDEA:
A Methodology for Supplier Evaluation and Selection in a
Supplier-Based Manufacturing Environment,” International
Journal of Manufacturing Technology and Management, Vol. 11,
No. 2, 2007, pp. 174-192.
Fowler, J.W., Sun,Y., and Shunk, D. “A Strategic Capacity
Allocation Game in the High-Tech Industry,” INFORMS
International Meeting 2007, Puerto Rico, July 8-11, 2007.
Leadership Activities
Editorial Board, International Journal of Flexible
Automation and Integrated Manufacturing; Editorial
Board, International Journal of Logistics; Editorial
Board, International Journal of Product Development.
D
an Shunk came from industry to ASU
in 1984 as an associate professor of
industrial engineering. From 1984 to
1994, he served as the CIM Systems Research
Center Director. He is currently serving as the
AVNET Chair of Supply Network Integration.
His principal research interests are in material,
information, knowledge supply network
integration, computer integrated manufacturing,
electronic
commerce
progression,
time
compression, cultural acceptance of change and
enterprise integration.
Shunk is a senior member of the Institute of
Industrial Engineers and a senior charter member
of the Computer Aided Systems Association of the
Society of Manufacturing Engineers. He is also a
member of the Alpha Pi Mu and Tau Beta Pi honor
societies. He currently serves on the editorial boards
and the International Journal of Flexible Automation
and Integrated Manufacturing, International Journal
of Logistics, and the International Journal of Product
Development.
Professors
19
Selected Publications
Ye, N. Secure Computer and Network Systems:
Modelings, Analysis and Design. London, UK:
John Wiley & Sons, 2008.
Xu, X. and Ye, N., “Minimization of job
waiting time variance on identical parallel
machines,” IEEE Transactions on Systems, Man,
and Cybernetics, Vol. 37, 2007, pp. 917-927.
Ye, N., and Chen, Q. “Attack-norm
separation for detecting attack-induced
quality problems on computers and
networks,” Quality and Reliability Engineering
International, Vol. 23, 2007, pp. 545-553.
Ye, N., Li, X., Farley, T. and Xu, X. “Job
scheduling methods for reducing waiting
time variance,” Computers & Operations
Research, Vol. 34, No. 10, 2007, pp. 30693083.
Nong Ye
Li, X.,Ye, N., Liu,T., Sun,Y. “Job Scheduling
to Minimize the Weighted Waiting Time
Variance of Jobs,” Computers & Industrial
Engineering, Vol. 2, 2007, pp. 41-56.
Professor
Ph.D., 1991, Purdue University
Information and systems assurance, data mining and modeling,
quality optimization and control of system operations
Information and Systems Assurance Laboratory: isa.eas.asu.edu
D
r. Ye’s past and current research activities–garnering over
$9M external funding and producing seventy-six journal
papers, two books, includingThe Handbook of Data Mining, and
one U.S. patent–fall into the following two areas: data and modeling,
and optimization and quality control of system operations.
Her research in data and modeling involves applications in computer
and network data, cognitive behavior data, and biomedical data.
Research in optimization and quality control of system operations
involves computer and network systems, and manufacturing and
supply chain enterprises.
Ye’s interdisciplinary research is bringing industrial engineering
theories and techniques into the scientific understanding and
engineering of information systems. Applications of her research are
establishing scientific understanding of information systems and the
human brain, and developing engineering technologies for secure and
dependable information systems.
20
Ye, N., Farley,T., and Lakshminarasimhan, D.
K. “An attack-norm separation approach for
detecting cyber attacks,” Information Systems
Frontiers, Vol. 8, 2007, pp. 163-177.
Ye, N., Li, X., Farley, T.R., Xu, X. “Job
Scheduling Methods for Reducing Waiting
Time Variance,” Computers & Operations
Research, Vol. 34, 2007, pp. 3069-3083.
Ye, N., Lai, Y.C., and Farley, T, “Quality
of Service Assurance for Dependable
Information Infrastructure,” Information
Security Research: New Methods for Protecting
Against Cyber Threats, Wiley, Chapter 1.2.1,
pp. 53-79, Indianapolis, Indiana, 2007.
Leadership Activities
Associate Editor, Information, Knowledge,
Systems Management; Editor, IEEE Transactions
on Systems, Man, and Cybernetics, Part A;
Editorial Board, International Journal of
Human-Computer Interaction; Editorial Board,
Information, Knowledge, Systems Management.
Arizona State University Industrial Engineering
Associate Professors
Mary Anderson-Rowland
Associate Professor
Ph.D., 1966, University of Iowa
Statistics and probability for quality control, academic scholarship programs for all engineering students with an emphasis on
women and underrepresented minority students
M
ary Anderson-Rowland is an associate professor in
the Department of Industrial Engineering in the Ira
A. Fulton School of Engineering at ASU. AndersonRowland received her B.A. in mathematics from Hope College
in 1961, and her M.S. and Ph.D. in mathematics/statistics from
the University of Iowa in 1963 and 1966, respectively.
Anderson-Rowland came to ASU in 1966 as a lecturer in
mathematics and became the first woman faculty in engineering
in 1974. She served as a statistical consultant to a variety of
industry from 1973 until 1993, when she became the first woman
appointed as an associate dean in the engineering school. She
served as the associate dean of Student Affairs for 11 years. She
is currently serving as the director of three academic scholarship
programs and a fourth project for transfer students.
Anderson-Rowland was heavily involved in the creation of
the Women in Engineering Program as well as the Minority
Engineering Program. She serves as a mentor for women and
underrepresented engineering students as well as supporting
research that increases the recruitment, enrollment, and
retention of engineering students with over 150 publications.
Anderson-Rowland has been the recipient of six national awards
and recognitions: American Society for Engineering Education,
Fellow, 2001; Distinguished Engineering Educator Award,
Society of Women Engineers, 2002; National Engineering
Award, 2003, the highest award given by the American
Association of Engineering Societies; SHPE National Educator
of the Year Star Award, 2005; Minorities in Engineering National
Award, American Society of Engineering Education, 2006; and
Society of Women Engineers, Fellow, 2006.
Selected Publications
Anderson-Rowland, M.R., Bernstein, B.L.
and Russo, N.F., “The Doctoral Program in
Engineering and Computer Science: Is It the
Same for Women and Men?” Proceedings of the
2007 WEPAN Conference, Orlando, Florida, June
2007, 14 pages, CD-ROM and www.wepan.
org.
Anderson-Rowland, M.R. and VanIngen-Dunn,
C., “Encouraging Transfer Students to Pursue a
Bachelor’s Degree in Engineering and Computer
Science,” Proceedings of the 2007 American Society
for Engineering Education Annual Conference &
Exposition, Honolulu, Hawaii, June 2007, 7
pages, CD-ROM and www.asee.org.
Anderson-Rowland, M.R., “A Comparison of
the Academic Achievements and Retention Rates
of Women and Men Engineering and Computer
Science Students in an Academic Scholarship
Program Designed for Underrepresented
Minority Students,” Proceedings of the 2007
WEPAN Conference, Orlando, Florida, June 2007,
11 pages, CD-ROM and www.wepan.org.
Leadership Activities
Associate
2007 WEPAN Proceedings Chair; 2006-2008
PIC IV Chair, Board of Directors, American
Society of Engineering Education; 2005 Women in
Engineering Division Chair, American Society of
Engineering Education; Women in Engineering
Recruitment and Retention Expert, National
Academy of Engineering.
Associate Professors
21
Selected Publications
Gel, E.S., Hopp, W.J., and Van Oyen, M.P.
“Hierarchical cross-training in WIP-constrained
environments,” IIE Transactions, 2007, 39(2), pp.
125 – 143.
Wirojanagud, P., Gel, E.S., J.W. Fowler,
and Cardy, R. “Modeling inherent worker
Differences for Workforce lanning,” International
Journal of Production Research, 45(3), 2007, pp.
525-553.
Vardar, C., Gel, E.S., Fowler, J.W. “A
framework for evaluating remote diagnostics
investment decisions for semiconductor
equipment suppliers,” European Journal of
Operational Research, 2007, 180(3), pp. 14111426.
Esma S. Gel
Associate Professor
Ph.D., 1999, Northwestern University
Applied probability, stochastic processes, queuing theory, stochastic
modeling and control of manufacturing systems
E
sma Gel researches and teaches courses in the area of operations
research, specifically focusing on production systems control and
supply chain management.
Her research focuses on the use of applied probability techniques for
management and design of production systems and supply chains. Some
of her recent work has been on workforce agility and management,
dynamic price and lead time quotation to manage congestion in maketo-order systems, queueing approximations for performance evaluation
of manufacturing systems, and economic impact of inventory record
inaccuracies in retail environments. Gel has presented her work in
national and international conferences, and published in leading archival
journals of her area. Her research has been funded by the National
Science Foundation (NSF), as well as industrial partners such as Intel,
IBM, and Infineon. Her latest grant from NSF involves the development
of a framework for the integration of price, lead time, order selection,
and inventory decisions to match supply with demand.
Gel is a member of the Institute for Operations Research and the
Management Sciences (INFORMS), the Institute of Industrial Engineers,
American Society of Engineering Education (ASEE), and the Operations
Research Society of Turkey.
22
Arizona State University Industrial Engineering
Gel, E.S., Hopp, W.J., and Van Oyen, M.P.
“Hierarchical cross-training in WIP-constrained
environments,” IIE Transactions, 2007, 39(2), pp.
125 – 143.
Armbruster, D. and Gel, E.S. “Bucket brigades
revisited: Are they always effective?” European
Journal of Operational Research, 2006, 172(1), pp.
213-229.
Gel, E.S., Hopp, W.J., and Van Oyen,
M.P. “Factors affecting the opportunity of
worksharing as a dynamic line balancing
mechanism,” IIE Transactions, 2002, 34(10), pp.
847-863.
Wirojanagud, P., Gel, E.S., Fowler, J.W., and
Cardy, R. “Modeling inherent worker differences
for workforce planning,” International Journal of
Production Research, 2007, 45(3), pp. 525-553.
Vardar, C., Gel, E.S., Fowler, J.W. “A framework
for evaluating remote diagnostics investment
decisions for semiconductor equipment
suppliers,” European Journal of Operational
Research, 2007, 180(3), pp. 1411-1426.
Carlyle, M.W., Fowler, J.W., Gel, E.S., and Kim,
B. “Quantitative comparison of approximate
solution sets for bi-criteria optimization
problems,” Decision Sciences, 2003, 34 (1), pp.
63-82.
Leadership Activities
Associate Editor, Journal of Flexible Services and
Manufacturing
Gerald Mackulak
Associate Professor
Ph.D., 1979, Purdue University
Simulation methodology, simulation output analysis,
automated production systems, material handling
design and analysis
Selected Publications
Bekki, J.E., Fowler, J.W., Mackulak, G.T., and Nelson, B.L.
“Using Quantiles in Ranking and Selection Procedures,”
Proceedings of the Winter Simulation Conference, Washington,
D.C., Dec. 9-12, 2007, pp. 1722-1728.
Lung, C.H., Urban, J.E., Mackulak, G.T. “Analogy-based
domain analysis approach to software reuse,” Requirements
Engineering, May 2006, pp. 1-22.
Mackulak, G.T., Fowler, J., Park, S., McNeill, J. “A Three
Phase Simulation Methodology for Generating Accurate
and Precise Cycle Time- Throughput Curves,” International
Journal of Simulation and Process Modeling, Vol. 1, Nos. 1/2,
2005, pp. 36-47.
Diaz, S., Fowler J.W., Pfund, M.E., Mackulak, G.T.,
and Hickie, M. “Evaluating the Impacts of Reticle
Requirements in Semiconductor Wafer Fabrication,” IEEE
Transactions on Semiconductor Manufacturing, Vol. 18, No. 4,
2005, pp.622-632.
Leadership Activities
Associate Editor, Transactions of the Society for Modeling
and Simulation International; Editorial Board, International
Journal of Simulation and Process Modeling; General Chair
2011, Winter Simulation Conference.
G
erald Mackulak is currently participating in
sponsored research from the SRC/International
Semitech. His collaborative research project is
investigating multi-product cycle time and throughput
evaluation via simulation on demand, sponsored by
Force II/SRC.
In previous years, he has participated in sponsored
research from the Semiconductor Research Corporation,
Anteon Corporation, Asyst, NSF, PRI Automation, the
Federal Highway Commission, the McDonnell Douglas
Corporation, the Hughes Missile Systems Company, the
Institute for Manufacturing and Automation Research,
the Allied-Signal Corporation, and Motorola.
Mackulak has written more than 75 journal and
conference papers. He was recently a member of the
editorial board of International Journal of Simulation and
Probability Modeling; a past associate editor for Simulation:
Transactions of the Society for Modeling and Simulation
International; and in 2003 edited a special issue of the
journal. He has received several Engineering Teaching
Excellence Award nominations. He currently serves as
the General Chair for the Winter Simulation Conference
in 2011.
Associate Professors
23
Selected Publications
Munoz, L., and Villalobos, J.R.
“Work Allocation Strategies for Serial
Assembly Lines under High Labor
Turnover,” International Journal of
Production Research, Vol. 40, No. 8, pp.
1835-1852, 2002.
Villalobos, J.R., Arellano, M., Medina,
A. and Aguirre, F. “Vector Classification
of SMD Images,” Journal of Manufacturing
Systems, Vol. 22, No. 4, pp. 265-282,
2003.
J. René Villalobos
Associate Professor
Ph.D., 1991, Texas A&M University
logistics, automated quality systems, manufacturing systems and applied operations research
International Logistics and Productivity Improvement Laboratory
(ILPIL): ilpil.asu.edu
R
ené Villalobos came to ASU in 1999 from the Mechanical and
Industrial Engineering Department at the University of Texas
at El Paso where he had been serving as an associate professor.
Prior to academia, Villalobos served as an industrial engineer for
Packard Electric and a project engineer for Renault Company. Sponsors
of Villalobos’ research include the National Science Foundation,
Texas Advanced Technology Program, the Arizona Department of
Transportation, U.S. Army and private industry, totaling an excess
of $3 million dollars. He was the recipient of the 1993 IIE Doctoral
Dissertation Award and a 1995 NSF Career Grant.
He is a member of Alpha Pi Mu, the Institute for Operations Research
and the Management Science, and the American Society for Engineering
Education. He is also a member of the Technical Advisory Board for
International Journal of Interactive Design and Manufacturing.
24
Arizona State University Industrial Engineering
Villalobos, J.R., Munoz, L., and
Gutierrez, M.A. “An Application of
Fixed and Adaptive Multivariate SPC
Charts for On-line Monitoring of
SMD Assembly,” International Journal of
Production Economics, Vol. 95, No. 1: pp.
109-121, 2005.
Van den Briel, M., Villalobos, J.R.,
Hogg, G.L., Lindeman, T., and Mule,
A. “Development of Efficient Boarding
Strategies at America West Airlines,”
Interfaces, Vol. 35, No. 3, pp. 191–201,
May–June 2005.
Garcia, H., Villalobos, R.J., and Runger,
G. “Automated Feature Selection
for Visual Inspection Systems,” IEEE
Transactions on Automation Science and
Engineering, Vol. 3, No. 4, pp. 394 –
406, October 2006.
Montano, A., Villalobos, J.R.,
Gutierrez, M.A., and Mar, L.R.
“Performance of Serial Assembly Line
Designs under unequal Operator Speeds
and Learning,” International Journal of
Production Research, Vol. 45 No 22, pp.
5355–5381, 2007.
Teresa Wu
Associate Professor
Ph.D., 2001, University of Iowa
Information systems, supply chain management,
multi-agent systems, data mining, Petri nets,
Kalman filtering
Intelligent Decision Systems Lab:
swag.fulton.asu.edu
T
eresa Wu came to the Ira A. Fulton
School of Engineering in 2001. In 2003,
she was the recipient of the National
Science Foundation’s Faculty Early Development
(CAREER) Award. Her research interests include
collaborative product development, supply chain
management, distributed decision support and
information systems. Wu’s CAREER project is the
“Design and Implementation of a Virtual Product
Development Environment.”
Wu’s has recently published her research in the
International Journal of Production Research, the
Journal of Computer Integrated Manufacturing, the
Journal of Production and Planning Control, the Journal
of Operations Management, ASME Transactions: Journal
of Computing and Information Science in Engineering,
International Journal of Concurrent Engineering:
Research and Applications, Computers in Industry.
Wu is a member of the Institute of Industrial
Engineers (IIE), the Society of Manufacturing
Engineering (SME) and the Institute for
Operations Research and the Management Science
(INFORMS).
Selected Publications
Wu, T., O’Grady, P. “An extended Kalman Filter for
Collaborative Supply Chains,” International Journal of
Production Research, Vol. 42, No. 12, 2004, pp. 2457-2475,
June 15.
Wu, T., Xie, N., Blackhurst, J.
“Design and
Implementation of Distributed Information System for
Collaborative Product Development,” ASME Transactions:
Journal of Computing and Information Science in Engineering,
Vol. 4, No. 4, 2004, pp. 281-293, Dec.
Wu,T.,Ye, N. and Zhang, D.W. “Comparison of Distributed
Methods for Resource Allocation,” International Journal of
Production Research, Vol. 43, No. 3, 2005, pp. 515-536.
Tseng, T-L., Jothishankar, M.C. and Wu, T. “Quality
Control Problem in Printed Circuit Manufacturing – a
Rough Set Based Approach,” Journal of Manufacturing
Systems, Vol. 23, No. 1, 2004, pp. 56-72.
Parmar, D., Wu, T., Blackhurst, J. “MMR: An Algorithm
for Clustering Categorical Data Using Rough Set Theory,”
Data and Knowledge Engineering, Vol. 63, Issue 3, Dec.
2007, pp. 879-893.
Leadership Activities
Editorial Board, International Journal of Electronic Business
Management; Editorial Board, Computer and Standard
Interface; Guest Editor, International Journal of Electronic
Business Management Special Issue on Enabling Distributed
Product Development.
Associate Professors
25
Assistant Professors
Selected Publications
Keha, A.B., deFarias, I.R., and Nemhauser,
G.L. “A Branch-and-Cut Algorithm without
Binary Variables for Nonconvex Piecewise
Linear Optimization,” Operations Research,
2006, 54, pp. 847-858.
Colak, A.B. and Keha, A.B. “Interval-Indexed
Formulation Based Heuristics for Single Machine Weighted Tardiness Problem,” Computers
and Operations Research, accepted for publication.
Balasubramanian, H., Fowler, J.W., Keha,
A.B. “Scheduling Interfering Job Sets on Parallel Machines,” European Journal of Operations
Research, accepted for publication.
Ahmet Keha
Assistant Professor
Ph.D., 2003, Georgia Institute of Technology
Computational and theoretical aspects of integer programming and
combinatorial optimization, modern heuristics techniques, logistics
and scheduling
Logistics, Optimization and Control Laboratory (LOC Lab)
A
hmet B. Keha joined the Ira A. Fulton School of Engineering
in 2003, after receiving his Ph.D. from the Georgia Institute
of Technology. His research interests include computational
and theoretical aspects of integer programming and combinatorial
optimization, application of integer programming, and modern heuristic techniques and scheduling.
Keha has presented papers at the INFORMS National Meetings,
International Symposium on Mathematical Programming and Industrial Engineering Research Conferences. Some of the journals
that he has published are Operations Research, the European Journal of
Operational Research, and Operations Research Letters.
26
Vielma, P.P., Keha, A.B., and Nemhauser,
G.L. “Nonconvex, lower semicontinuous
piecewise linear optimization,” Discrete Optimization, Volume 5, 2008, pp. 467-488.
Keha, A.B, de Farias, I.R, and Nemhauser,
G.L. “ A Branch-and-Cut Algorithm without
Binary Variables for Nonconvex Piecewise
Linear Optimization,” Operations Research, Vol
54, 2006, pp. 847-858.
Keha, A.B., Khowala, K., and Fowler, J.W.
“Mixed Integer Programming Formulations
for Non-preemptive Single Machine Scheduling Problems,” Computers and Industrial Engineering, accepted for publication.
Laub, J.D., Fowler, J.W., and Keha, A.B.
“Minimizing makespan with multiple orders
per job in a two machine flowshop,” European
Journal of Operational Research, Volume 182,
2007, pp.s 63-79
Assistant
Arizona State University Industrial Engineering
Selected Publications
Jin, R., Li, J., and Shi, J. “Quality
Prediction and Control in Rolling
Processes using Logistic Regression,”
Transactions of NAMRI/SME (North
American Manufacturing Research Institution
of Society of Manufacturing Engineers), 35,
2007, pp. 113-120.
Li, J., Shi, J., and Chang, T.S. “On-line
Seam Detection in Rolling Processes using
Snake Projection and Discrete Wavelet
Transform,” ASME (American Society of
Mechanical Engineers) Transactions, Journal
of Manufacturing Science and Engineering,
129 (5), 2007, pp. 926-933.
Jing Li
Assistant Professor
Ph.D., 2007, University of Michigan
Applied statistics, process control, data mining,
causal modeling and inference
Quality and Reliability Engineering Laboratory (Q&RE lab)
J
ing Li joined the Industrial Statistics research group in Fall 2007.
Li’s research interests include applied statistics, data mining, causal
modeling and inference for process control. Her recent research
focuses on modeling and analyzing massive high-dimensional
datasets in complex systems for improving the quality of products and
processes. Her work has been applied to manufacturing and public health
problems.
Lin, G., Li, J., Hu, S. J., and Cai, W. “A
Computational Response Surface Study of
3D Aluminum Hemming using Solid-toShell Mapping,” ASME (American Society of
Mechanical Engineers) Transactions, Journal
of Manufacturing Science and Engineering,
129 (2), 2007, pp. 360-368.
Li, J., and Shi, J. “Knowledge Discovery
from Observational Data for Process
Control using Causal Bayesian Networks,”
IIE (Institute of Industrial Engineers)
Transactions, 39 (6), 2007, pp. 681-690.
She recently received an IERC Best Paper award for “Causation-Based
T2 Decomposition for Multivariate Process Monitoring and Diagnosis,”
co-authored with Judy Jin and her advisor, Jan Shi, at the 2006 IIE
Conference.
Li is a member of the Institute for Operations Research and the
Management Sciences (INFORMS) and the Institute of Industrial
Engineers (IIE).
Assistant Professors
27
Rong Pan
Assistant Professor
Ph.D., 2002, Pennsylvania State University
Industrial statistics, reliability analysis and time series modeling
Quality and Reliability Engineering Laboratory (Q&RE lab)
R
Selected Publications
Zhao, W., Pan, R., Aron, A. and Mettas, A. “Some
Properties of Confidence Bounds on Reliability
Estimation for Parts under Varying Stresses,” IEEE
Transactions on Reliability, 55(1): 7-17, 2006.
Colosimo, B.M., Pan, R. and del Castillo, E. “Setup
Adjustment for Discrete-Part Processes under
Asymmetric Cost Functions,” International Journal of
Production Research, 43(18): 3837-3854, 2005.
Pan, R. and Batres, J. “Product Reliability Prediction
with Failure Information Fusion,” 2007 Proceedings of
the 13th ISSAT International Conference on Reliability and
Quality in Design, 102-106.
Leadership Activities
Associate Editor, Journal of Quality Technology
28
ong Pan joined the Department of Industrial
Engineering in the Ira A. Fulton School of Engineering
in 2006. He received his B.S. in Materials Engineering
from Shanghai Jiao Tong University, China, in 1995; his M.S.
in Industrial Engineering from the College of Engineering of
Florida A&M University and the Florida State University in 1999;
and his Ph.D. in Industrial Engineering from the Pennsylvania
State University in 2002. Before coming to ASU, Pan was an
assistant professor of Industrial Engineering at the University of
Texas at El Paso.
Pan’s research interests include statistical quality control,
reliability engineering, time series analysis and control,
and supply chain management. Journals he has published in
include Journal of Quality Technology, Journal of Applied Statistics,
International Journal of Production Research, and Quality and
Reliability Engineering International. His current research project,
funded by the National Science Foundation (NSF), is on modeling
and analysis of profiled reliability testing using computationintensive statistical methods. His previous projects were funded
by U.S. Department of Education (DoEd), Texas Department of
Transportation (TxDOT) and GM.
Pan is a senior member of American Society of Quality (ASQ),
and a member of the Institute for Operations Research and
the Management Sciences (INFORMS), Institute of Industrial
Engineering (IIE), and Institute of Supply Management (ISM).
He is currently serving as an associate editor of Journal of Quality
Technology.
Arizona State University Industrial Engineering
Selected Publications
Zhang, M., and Atamtürk, A. “The Flow
Set with Partial Order,” forthcoming
in Mathematics of Operations Research,
2008.
Zhang, M., and Atamtürk, A. “TwoStage Robust Network Flow and Design
for Demand Uncertainty,” Operations
Research, 2007, Vol. 55, pp.662-673.
Muhong Zhang
Assistant Professor
Ph.D., 2006, University of California, Berkeley
Integer programming, robust optimization, computational optimization, and
network optimization
M
uhong Zhang joined the Department of Industrial
Engineering in 2007 after completing a lecturer appointment
at the University of California, Berkeley. Her past and
present research work has been on developing techniques for robust
optimization, transportation, and distribution in logistics, mixed-integer
programming, combinatorial optimization, and network flows. Her
work has been studying the two-stage robust network flow and design
problem with demand uncertainty.
In the first stage, integer capacity decisions and flows on a subset of
the arcs are determined. The recourse flow is determined in the
second stage, after the realization of the uncertain demands. The robust
network flow and design problem has many potential applications in
telecommunication, hub location, production, and distribution logistics.
Her research on two-stage robust network flow/design problem is for
the general problem; currently, she is working on applications of this
work to problems with special network structures.
Zhang, M. “The Rubust 0-1 Knapsack
Polyhedron,” INFORMS Annual Meeting,
Seattle, WA, Nov. 2007.
Atamtürk, A., and Zhang, M. “TwoStage Robust Network Flow and Desing
for Demand Uncertainty,” Operations
Research, 2005.
Gu, J., Hu, X., Jia, X., and Zhang,
M. “Routing Algorithm for Multicast
under Multi-tree Model in Optical
Networks,” Theoretical Computer Science,
Elsevier Science Publishers B. V., the
Netherlands, 314(1-2)m, 2004, pp.
293-301.
Gu, J., Hu, X.D., Zhang, M.
“Algorithms for multicast connection
under multi-path routing model,”
Information Processing Letters, 84 (1),
October 2002, pp. 31-39.
Assistant Professors
29
Burak Büke
Alla Kammerdiner
Visiting Assistant Professor
Visiting Assistant Professor
B
A
urak Büke earned his Ph.D. in operations
research and industrial engineering
from The University of Texas at Austin
in December, 2007. He has an M.S.E. in
operations research and industrial engineering
and a B.S. in industrial engineering. His research
interests include: queueing and fluid networks;
makespan and holding cost problems in complex
manufacturing environments; applications of
stochastic programming; stochastic optimization
algorithms; revenue management problems
arising in airlines, hospitality and entertainment
industries; and pattern recognition and statistical
data analysis.
Selected publications:
Büke, B., Hasenbein, J.J., and Morton, D.P. “Minimizing
Makespan for a Multiclass Fluid Network with Parameter
Uncertainty,” Probability in Engineering and Informational
Sciences, accepted for publication.
Büke, B., Kuyumcu, H.A., and Yildirim, U. “New Stochastic
Programming Approximations to Network Capacity Control
Problem with Buy-ups,” Journal of Revenue and Pricing
Management, 7(1), 2008, pp. 61-84.
lla Kammerdiner earned her Ph.D.
in industrial and systems engineering
from the University of Florida in May,
2008. She has an M.S. in Mathematics, 2004,
also from University of Florida, and a B.S. in
Probability Theory and Mathematical Statistics,
1999, from National Taras Shevchenko University
of Kiyv, Ukraine. Her research interests include
data mining and its applications in biomedicine,
global and combinatorial optimization, financial
engineering, Bayesian networks, probability
theory and mathematical statistics.
Selected publications:
Kammerdiner, A.R. “Bayesian networks.” In Floudas, C.A.,
Pardalos, P.M., editors Encyclopedia of Optimization, 2nd
ed., 2008, Springer (to appear).
Zhang, Z.Q., Kammerdiner, A.R., Busygin, S., Ottens,
A.K., Larner, S.F., Kobeissy, F.H., Pardalos, P.M., Hayes,
R.L., and Wang, K.K. “Applications of the data mining
techniques to the systems biology of neruitogenesis,”
Optimization Methods and Software, Vol. 22 (1), 2007, pp.
215-224.
visiting
Büke, B., Ercil, A., and Oden, C. “Combining implicit
polynomials and geometric features for hand recognition,”
Pattern Recognition Letters, 24(13), 2003, pp. 2145-2152.
30
Arulselvan, A., Boginski,V., Kammerdiner, A., and Pardalos,
P.M. “Analysis of stock market structure by identifying
connected components in the market graph,” Journal of
Financial Decision Making, Vol. 1 (1), 2005, pp. 27-37.
Arizona State University Industrial Engineering
2007 Publications
Refereed Journal Articles
Armbruster, D., Gel, E. S., and Murakami, J.,
“Bucket brigades with worker learning,” European
Journal of Operational Research, Vol. 176, pp. 264-274,
2007.
Bayraktar, E., Jothishankar, M.C., Wu, T., “Evolution
of Operations Management,” Management Research
News, Vol. 30, No. 11, pp. 843-871, 2007.
Berrado, A., and Runger, C. G., “Using Metarules to
Organize and Group Discovered Association Rules,”
Data Mining and Knowledge Discovery, Vol. 14, No. 3,
pp. 409-431, 2007.
Chung, P. J., Goldfarb, H. B., and Montgomery,
D. C., “Optimal Designs for Mixture-Process
Experiments with Control and Noise Factors”,
Journal of Quality Technology, Vol. 39, No. 3, pp. 179190, 2007.
Duarte, B., Fowler, J.W., Knutson, K., Gel,
E., and Shunk, D., “A Compact Abstraction of
Manufacturing Nodes in a Supply Network,”
International Journal of Simulation and Process Modeling,
Vol. 3, Nos. 3, pp. 115-126, 2007.
Gel, A., Pannala, S., Syamlal, M., O’Brien, T. J.,
and Gel, E. S., “Comparison of Frameworks for
Next Generation Multiphase Flow Solver, MFIX: A
Group Decision-Making Exercise,” Concurrency and
Computation: Practice and Experience, Vol. 19, pp. 609624, 2007.
Gel, E. S., Hopp, W. J., and Van Oyen, M. P.,
“Hierarchical cross-training in Work-In-ProcessConstrained Environments,” IIE Transactions, 39(2),
pp. 125-143, 2007.
Holcomb, D.R., Montgomery, D. C., and Carlyle,
W.M., “The use of Supersaturated Experiments in
Turbine Engine Development”, Quality Engineering,
Vol. 19, No. 1, pp. 17-27, 2007.
Hu, J., Runger, G.C., and Tuv, E., “Tuned Artificial
Contrasts to Detect Signals,” International Journal of
Production Research: Special Issue on Control Charts, Vol.
45, No. 23, pp. 5527 – 5534, 2007.
Hwang, W., Runger, G.C., and Tuv, E., “Multivariate
Statistical Process Control with Artificial Contrasts,”
IIE Transactions: Special Issue on Data Mining, Vol. 39,
No. 6, pp. 659-669, 2007. IIE Transactions on
Quality and Reliability Engineering Best Application
Paper Award 2007.
of manufacturing systems,” International Journal of
Production Research, Vol. 45, Num 2, pp. 267 – 285,
2007.
Jin, R., Li, J., and Shi, J., “Quality Prediction
and Control in Rolling Processes using Logistic
Regression,” Transactions of NAMRI/SME (North
American Manufacturing Research Institution of Society of
Manufacturing Engineers), 35, pp. 113-120, 2007.
Kwon,Y-J., Wu, T., “Cognitive Understanding of
Remote Systems from the Perspectives of Online
Laboratory Learning,” ASEE: Computers in Education
Journal, Vol XVII, No.3, pp. 93-105, July – Sep.,
2007.
Laub, J.D., Fowler, J.W., and Keha, A.B.,
“Minimizing Makespan with Multiple Orders per
Job in a Two Machine Flowshop”, European Journal
of Operational Research, Vol. 182, No. 1, pp. 63-79,
2007.
Lawson, C. and Montgomery, D.C., “A Logistic
Regression Modeling Approach to Business
Opportunity Assessment”, International Journal of Six
Sigma and Competitive Advantage, Vol. 3, No. 2, pp.
120-136, 2007.
Li, J., and Shi, J., “Knowledge Discovery from
Observational Data for Process Control using
Causal Bayesian Networks,” IIE (Institute of Industrial
Engineers) Transactions, 39 (6), pp. 681 – 690, 2007.
Li, J., Shi, J., and Chang, T.S., “On-line Seam
Detection in Rolling Processes using Snake
Projection and Discrete Wavelet Transform,” ASME
(American Society of Mechanical Engineers) Transactions,
Journal of Manufacturing Science and Engineering,
129(5), pp. 926-933, 2007.
Li, X.,Ye, N., Liu, T., Sun,Y., “Job Scheduling to
Minimize the Weighted Waiting Time Variance of
Jobs,” Computers & Industrial Engineering, Vol. 2, pp.
41-56, 2007.
Li, X.,Ye, N., Xu, X., Sawhey, R., “Influencing
Factors of Job Waiting Time Variance on a Single
Machine,” European Journal of Industrial Engineering,
Vol. 1, pp. 56-73, 2007
Lin, G., Li, J., Hu, S. J., and Cai, W., “A
Computational Response Surface Study of 3D
Aluminum Hemming using Solid-to-Shell Mapping,”
ASME (American Society of Mechanical Engineers)
Transactions, Journal of Manufacturing Science and
Engineering, 129(2), pp. 360-368, 2007.
Jearkpaporn, D., Borror, C.M., Runger, G.C., and
Montgomery, D.C. , “Process Monitoring for Mean
Shifts for Multiple Stage Processes”, International
Journal of Production Research, Vol. 45, No. 23, pp.
5547-5570, 2007.
Mönch, L., Schabacker, R., Pabst, D., and Fowler,
J.W., “Genetic Algorithm-Based Subproblem
Solution Procedures for a Modified Shifting
Bottleneck Heuristic for Complex Job Shops”,
European Journal of Operational Research, Vol. 177, No.
3, pp. 2100-2118, 2007.
Jeong, I-J., Leon, J.V., Jorge, V., and Villalobos,
J. R., “Integrated decision support system for
diagnosis, maintenance planning and scheduling
Montano, A., Villalobos, J.R., Gutierrez, M.A.,
and Mar, L.R., “Performance of Serial Assembly
Line Designs under unequal Operator Speeds and
Learning,” International Journal of Production Research,
Vol. 45 No 22, pp. 5355–5381, 2007.
Parmar, D., Wu, T., Blackhurst, J., “MMR: An
Algorithm for Clustering Categorical Data Using
Rough Set Theory,” Data and Knowledge Engineering,
Vol. 63, Issue 3, pp. 879-893, Dec. 2007.
Perry, L. A., Montgomery, D. C., and Fowler, J. W.,
“A Partition Experimental Design for a Sequential
Process with a Large Number of Variables”, Quality
and Reliability Engineering International, Vol. 23, No.
5, pp. 555-564, 2007.
Runger, G.C., Barton, R.R., Castillo, E. Del, and
Woodall, W.H., “Optimal Monitoring of Multivariate
Data for Fault Patterns,” Journal of Quality Technology,
Vol. 39, No. 2, pp. 159-162, 2007.
Shunk, D., Carter, J., Hovis, J. and Talwar, A., “The
Drivers of Intermediation and Disintermediation
when the Industry is Under Stress,” International
Journal of Physical Distribution and Logistics Management,
Vol. 37, No. 3, pp 248-261, 2007.
Swaminathan, R., Pfund, M.E., Fowler, J.W.,
Mason, S.J., and Keha, A., “Impact of Permutation
Enforcement when Minimizing Total Weighted
Tardiness in Dynamic Flowshops with Uncertain
Processing Times,” Computers and Operations Research,
Vol. 34, No. 10, pp. 3055-3068, 2007.
Vardar, C., Gel, E.S., and Fowler, J.W., “A
Framework for Evaluating Remote Diagnostics
Investment Decisions for Semiconductor Equipment
Suppliers,” European Journal of Operational Research,
Vol. 180, No. 3, pp. 1411–1426, 2007.
Villalobos, J.R, “The Stock Portfolio Game,”
INFORMS Transactions on Education, Vol. 8, No. 1,
2007.
Wirojanagud, P., Gel, E.S., Fowler, J.W., and
Cardy. R., “Modeling Inherent Worker Differences
for Workforce Planning”, International Journal of
Production Research, Vol. 45, No. 3, pp. 525 – 553,
2007.
Wu, T., Blackhurst, J., and O’Grady, P., “A
Methodology for Supply Chain Disruption Analysis,”
International Journal of Production Research, Vol. 45,
No. 7, pp. 1665-1682, April. 2007.
Wu, T., Blackhurst, J., Shunk, D., Appalla, R.,
“AIDEA: A Methodology for Supplier Evaluation
and Selection in a Supplier-Based Manufacturing
Environment,” International Journal of Manufacturing
Technology and Management, Vol. 11, No. 2, pp. 174192, 2007.
Xu, X.,Ye, N., “Minimization of Job Waiting Time
Variance on identical parallel machines,” IEEE
Transactions on Systems, Man, and Cybernetics, Vol. 37,
pp. 917-927, 2007.
Ye, N., Chen, Q., “Attack-Norm Separation
Fordetecting Attack-Induced Quality Problems on
2007 Publications
31
Computers and Networks,” Quality and Reliability
Engineering International, Vol. 23, pp. 545-553, 2007.
Ye, N., Farley, T., and Lakshminarasimhan, D. K.,
“An attack-norm separation approach for detecting
cyber attacks,” Information Systems Frontiers, Vol. 8, pp.
163-177, 2007.
Ye, N., Li, X., Farley, T. R., Xu, X., “Job Scheduling
Methods for Reducing Waiting Time Variance,”
Computers & Operations Research, Vol. 34, pp. 30693083, 2007.
Zhang, M., and AtamtÄurk, A., “Two-Stage Robust
Network Flow and Design for Demand Uncertainty,”
Operations Research, Vol. 55, pp. 662-673, 2007.
Bachelor’s Degree in Engineering and Computer
Science,” Proceedings of the 2007 American Society for
Engineering Education Annual Conference & Exposition,
Honolulu, Hawaii, June 2007, 7 pages, www.asee.
org.
Pan, R., Solis, A. and Paul, B., “Demand-Supply
Interaction and Production Capacity Planning for
Short Life Cycle Products,” Proceedings of the 36th
Annual Meeting of theWestern Decision Sciences Institute,
2007.
Askin, R. G., D. Pabst, Pew, M., and Sun,Y., “Using
Real Time Information in Operational Planning
and Control,” Proceedings of the 19th International
Conference on Production Research, Valparaiso, Chile,
2007, 6 pages.
Ye, N., Lai,Y. C., and Farley, T, “Quality of
Service Assurance for Dependable Information
Infrastructure,” Information Security Research: New
Methods for Protecting Against Cyber Threats, Wiley,
Chapter 1.2.1, pp. 53-79, Indianapolis, Indiana,
2007.
Bekki, J. E., Fowler, J.W., Mackulak, G.T. and
Nelson, B.L., “Using Quantiles in Ranking and
Selection Procedures,” Proceedings of theWinter
Simulation Conference, Washington, DC, Dec. 9-12,
2007, pp. 1722-1728.
Conference Proceedings,
Book Chapters
Chatlani, V. P., Tylavsky, D. J., Montgomery, D. C.,
and Dyer, M., “Statistical Properties of Diversity
Factors for Probabilistic Loading of Distribution
Transformers,” 39th North American Power Symposium
(NAPS 2007), pp. 581-587.
Anderson-Rowland, M.R., “A Comparison of the
Academic Achievements and Retention Rates of
Women and Men Engineering and Computer
Science Students in an Academic Scholarship
Program Designed for Underrepresented Minority
Students,” Proceedings of the 2007WEPAN Conference,
Orlando, Florida, June 2007, 11 pages, CD-ROM,
and www.wepan.org.
Guo, H., and Pan, R., “D-Optimal Reliability
Test Design for Two Stress Accelerated Life Tests,”
Proceedings of the IEEE International Conference on
Industrial Engineering and Engineering Management,
1236-1241, 2007.
Anderson-Rowland, M.R., Bernstein, B.L., & Russo,
N.F., “Encouragers and Discouragers for Domestic
and International Women in Doctoral Programs in
Engineering and Computer Science,” Proceedings of
the 2007 American Society for Engineering Education
Annual Conference & Exposition, Honolulu, Hawaii,
June 2007, 13 pages, www.asee.org.
Anderson-Rowland, M.R., Bernstein, B.L., &
Russo, N.F., “The Doctoral Program in Engineering
and Computer Science: Is It the Same for Women
and Men?” Proceedings of the 2007WEPAN Conference,
Orlando, Florida, June 2007, 14 pages, CD-ROM,
and www.wepan.org.
Anderson-Rowland, M.R., & Culley, P.I., “Helping
Lower Division Engineering Students Develop a
Good Resume,” Proceedings of the 2007 American
Society for Engineering Education Annual Conference &
Exposition, Honolulu, Hawaii, June 2007, 8 pages,
www.asee.org.
Anderson-Rowland, M.R., & Newell, D.C., “A
Three Year Evaluation of a NACME Program,”
Proceedings of the 2007 American Society for Engineering
Education Annual Conference & Exposition, Honolulu,
Hawaii, June 2007, 8 pages, www.asee.org.
Anderson-Rowland, M.R., & Rowland, J.R., “The
Correlation between GPA and Percent Effort on the
Guaranteed 4.0 Plan,” 37th ASEE/IEEE Frontiers in
Education Conference, Milwaukee, WI, October 2007,
6 pages, http://fie.engrng.pitt.edu/fie2007/index.
html
Anderson-Rowland, M.R., & VanIngen-Dunn,
C., “Encouraging Transfer Students To Pursue a
32
Habla, C., Mönch, L., Pfund, M.E., and Fowler,
J.W., “A Decomposition Heuristic for Planning and
Scheduling of Jobs on Unrelated Parallel Machines,”
Proceedings of the 3rd Multidisciplinary International
Conference on Scheduling:Theory and Applications, 2007,
pp. 112-119.
Hu, J., Runger, G.C., and Tuv, E., “Contributors
to a Signal from an Artificial Contrast,” Informatics
in Control, Automation and Robotics II, pp. 71-78,
Springer, Netherlands, 2007.
Huschka, T.R., Denton, B.T., Gul, S., and Fowler,
J.W., “Bi-Criteria Evaluation of an Outpatient
Procedure Center via Simulation,” Proceedings of the
Winter Simulation Conference, Washington, DC, Dec.
9-12, 2007, pp. 1510-1518.
Laub, J.D., Fowler, J.W., and Keha, A.B.,
“Minimizing Makespan with Multiple Orders per
Job in Mixed Flowshops,” Proceedings of the 3rd
Multidisciplinary International Conference on Scheduling:
Theory and Applications, 2007, pp. 301-308.
Li, X., and Ye, N., Chapter 16, “Intrusion detection
and information infrastructure protection,” In,
Information Systems, Vol. 2. Information Security.
Elsevier, 2007.
Marquis, J., Fowler, J., Gel, E. S., Köksalan,
Korhonen, P., and Wallenius, J., “Interactive
Evolutionary Multicriteria Scheduling. In (Ed.),”
the 3rd Multidisciplinary International Conference on
Scheduling:Theory and Applications, pp. 591-594, 2007.
Pan, R., and Batres, J., “Product Reliability
Prediction with Failure Information Fusion,”
Proceedings of the 13th ISSAT International Conference on
Reliability and Quality in Design, 102-106, 2007.
Arizona State University Industrial Engineering
Ye, N., and Zhao, L., “Onset of Traffic Congestion
in Complex Networks,” Information Security Research:
New Methods for Protecting Against Cyber Threats. Wiley,
Chapter 1.2.2, pp. 80-87, Indianapolis, Indiana,
2007.
Books Authored
Montgomery, D. C., and Runger, G. Applied Statistics
and Probability for Engineers, 4th Edition. John Wiley &
Sons. 768 pages, New York, 2007.
Montgomery, D. C., Runger, G. C., and Hubele,
N. F. Engineering Statistics, 4th edition. John Wiley &
Sons, New York, 2007.
Ye, N. Secure Computer and Network Systems: Modeling,
Analysis and Design. John Wiley & Sons. 336 pages,
New York, 2007.
Conference Proceedings
Anderson-Rowland, M.R., “A Comparison of the
Academic Achievements and Retention Rates of
Women and Men Engineering and Computer
Science. Students in an Academic Scholarship
Program Designed for Underrepresented Minority
Students,” Invited Presentation: NACME National
Conference, Best Practices, Denver, CO, 7/29/07.
Anderson-Rowland, M.R., “A Comparison of the
Academic Achievements and Retention Rates of
Women and Men Engineering and Computer
Science Students in an Academic Scholarship
Program Designed for Underrepresented Minority
Students,” Proceedings of the 2007 WEPAN Conference,
Orlando, Florida, June 2007, 11 pages, CD-ROM,
and www.wepan.org.
Anderson-Rowland, M.R., Invited Presentation:
“Academic Leadership,” Society ofWomen Engineers
(SWE) National Conference, Nashville, Tennessee,
October, 2007.
Anderson-Rowland, M.R., Invited Presentation:
“Is Graduate School for You?” the SWE National
Conference, Oct. 24-28, 2007, Nashville, Tennessee.
Anderson-Rowland, M.R., Bernstein, B.L., & Russo,
N.F., “The Doctoral Program in Engineering and
Computer Science: Is It the Same for Women and
Men?” Proceedings of the 2007WEPAN Conference,
Orlando, Florida, June 2007, 14 pages, CDROM, and www.wepan.org.
Anderson-Rowland, M.R., Bernstein, B.L., &
Russo, N.F., “Encouragers and Discouragers
for Domestic and International Women
in Doctoral Programs in Engineering and
Computer Science,” Proceedings of the 2007
American Society for Engineering Education Annual
Conference & Exposition, Honolulu, Hawaii, June
2007, 13 pages, www.asee.org.
Anderson-Rowland, M.R., & Culley, P.I.,
“Helping Lower Division Engineering Students
Develop a Good Resume,” Proceedings of the
2007 American Society for Engineering Education
Annual Conference & Exposition, Honolulu,
Hawaii, June 2007, 8 pages, www.asee.org.
Anderson-Rowland, M.R., & Newell,
D.C., “A Three Year Evaluation of a NACME
Program,” Proceedings of the 2007 American
Society for Engineering Education Annual
Conference & Exposition, Honolulu, Hawaii, June
2007, 8 pages, www.asee.org.
Anderson-Rowland, M.R., & VanIngen-Dunn,
C., “Encouraging Transfer Students To Pursue
a Bachelor’s Degree in Engineering and
Computer Science,” Proceedings of the 2007
American Society for Engineering Education Annual
Conference & Exposition, Honolulu, Hawaii, June
2007, 7 pages, www.asee.org.
Askin, R. G. “Research Needs in Workforce
Engineering,” Panel Discussion, INFORMS,
Seattle, WA, 2007.
Askin, R. G., “Human Issues in Forming and
Operating Cells,” Department of Industrial
Engineering, Auburn University, 2007 (invited).
Askin, R. G., “Modeling the Effect of Random
Outcomes in Multistage Decision Processes,”
International INFORMS Conference, Puerto Rico,
2007.
Bernstein, B.L., Russo, N.F., and AndersonRowland, M.R., “Everyday discouragers and
supports for women in STEM PhD programs,”
In Bernstein, B.L. (symposium organizer),
Predictors of Science and Engineering Involvement:
Three NSF-Funded Studies, Annual Meeting of the
American Psychological Association, San Francisco,
CA, August 2007.
Borisov, A., Runger G., and Eugene T.,
“Contributor Diagnostics for Process Monitors
from Artificial Contrasts,” Data Mining
Workshop, INFORMS National Conference, Seattle,
WA, 2007.
Fowler, J.W., Gel, E., Köksalan, M.,
Korhonen, P., Marquis, J., and Wallenius,
J., “Interactive Evolutionary Multiobjective
Optimization for Quasi-Concave Preference
Functions,” DSI, Phoenix, Nov. 17-20, 2007.
Fowler, J.W., Gel, E., Köksalan, M.,
Korhonen, P., Marquis, J., and Wallenius,
J., “Interactive Evolutionary Multicriteria
Scheduling,” INFORMS, Seattle, Nov. 4-7,
2007.
Fowler, J., Gel, E. S., and Wirojanagud, P.,
“Workforce Planning Models with Individual
Worker Differences,” INFORMS Annual Meeting,
Seattle, WA, November 17- 20, 2007.
presented at the Joint Statistical Meetings, Salt
Lake City, 31 July, 2007.
Montgomery, D.C., “Statistics and Science,
Business and Industry,” Invited Keynote
Presentation at the JMP Users’ Conference, Cary
NC, 13 June 2007.
Fowler, J.W., Sun,Y., and Shunk, D., “A
Strategic Capacity Allocation Game in the
High-Tech Industry,” INFORMS International
Meeting 2007, Puerto Rico, July 8-11, 2007.
Montgomery, D. C., “Teaching DOX: Some
Adventures and Lessons Learned,” Invited
Plenary Presentation at the ASA Quality and
Productivity Research Conference, Santa Fe, New
Mexico, 4 June 2007.
Ganguly, S., Keha, A., and Wu, T., “Penalty
Function Approach for Compromise
Mechanisms in Distributed Collaborative
Design Optimization,” INFORMS National
Meeting, Seattle, November 2007.
Pan, R., and Gamez, H., “Effects of Common
Cause Failure on the System Subject to
Competing Risks,” presented at the 51st ASA/
ASQ Fall Technical Conference, Jacksonville, FL.,
2007.
Garcia, H,C., and Villalobos, J.R., “A Novel
Feature Selection Method for the Quadratic
Discriminant Function,” INFORMS Annual
Meeting, Seattle, WA, November 4-7, 2007.
Runger, G., Invited presentation, Intel
Corporate Statistical Summit, September, 2007.
García, H.C., and Villalobos, J.R., “A
Reconfigurable Framework for Automated
Visual Inspection Systems,” INFORMS
International Meeting 2007, San Juan, Puerto
Rico, July 8-11, 2007.
Guo, H. and Pan, R., “Optimal Reliability
Tests for Multiple Stresses,” presented at 2007
INFORMS, Seattle, WA.
Ho, J. C., Tseng, T.-L., and Pan, R.,
“Green Supply Chain Management and its
Opportunities,” presented at the Southeast
INFORMS, 2007.
Hu, J., Runger, G.C., and Tuv, E., “SelfLearning of Decision Rules for Statistical
Process Control,” the National Science
Foundation CMMI Conference, Knoxville, TN,
2007.
Runger, G., Invited Presentation: Data Mining
Research, Weyerhaeuser Research Meeting,
August, 2007, Seattle, WA
Runger, G.C., and Tuv, E., “Feature Selection
with Ensembles for Complex Systems,” the
National Science Foundation CMMI Conference,
Knoxville, TN, 2007.
Sanchez, O. and Villalobos, J.R., “Design of
a Logistics Platform for the Distribution of
Fresh Produce,” INFORMS Annual Meeting,
Seattle, WA, November 4-7, 2007.
Villalobos, J.R., “The Stock Portfolio
Classroom Game,” INFORMS Annual Meeting,
Seattle, WA, November 4-7, 2007.
Villalobos, J.R., “Productivity Based Incentives
for Dynamic Work Allocation Systems,”
INFORMS Annual Meeting, Seattle, WA,
November 4-7, 2007.
Keha, A., “Using Integer Programming for
Solving Problems in Information Theory,”
Invited Talk at the INFORMS National Meeting,
Seattle, November 2007.
Villalobos, J.R., “Productivity Improvement
Opportunities in Industry,” Presentation to
industry and students, Texas State University,
San Marcos, TX, November 9 2007.
Keha, A., Balasubramanian, H., and Fowler,
J., “Bicriteria Scheduling of Equal Length
Jobs with Release Dates on Identical Parallel
Machines,” INFORMS National Meeting, Seattle,
November, 2007.
Villalobos, J.R., “The Stock Game,”
Presentation to Industrial Engineering
students, Texas State University, San Marcos,
TX, November 9 2007.
Keha, A., Colak, A., and Haralson, S.,
“Improving Airline Schedule Planning at Swift
Aviation Group,” INFORMS National Meeting,
Seattle, November, 2007.
Montgomery, D.C., Invited keynote Address,
“The Modern Practice of Statistics in Business
and Industry,” Swiss Statistics Meeting, Lucerne,
Switzerland, 14-16 November, 2007.
Montgomery, D.C., “A Modern Framework
for Enterprise Excellence,” Deming Lecture,
Villalobos, J.R., and Ahumada, O.,
“Development of Planning Tools for the Supply
Chain of Fresh Produce,” INFORMS Annual
Meeting, Seattle, WA, November 4-7, 2007.
Zhang, M., “The Robust 0-1 Knapsack
Polyhedron,” INFORMS Annual Meeting, Seattle,
WA. Nov. 2007.
ie faculty
Mary R. Anderson-Rowland, Ph.D.
Statistics and probability for quality control,
academic scholarship programs for all
engineering students with an emphasis
on women and underrepresented minority
students.
Ronald G. Askin, Ph.D.
Design and operation of discrete
manufacturing systems, production systems,
decision analysis, applied operations
research, facilities planning, industrial
statistics and applied optimization.
Gary L. Hogg, Ph.D.
Applied
optimization,
simulation,
manufacturing planning and control.
Ahmet B. Keha, Ph.D.
Computational and theoretical aspects of
integer programming and combinatorial
optimization, modern heuristics techniques,
logistics and scheduling.
Jing Li, Ph.D.
Applied statistics, process control, data
mining, causal modeling and inference.
Linda Chattin, Ph.D.
Discrete optimization, stochastic processes
and probabilistic modeling, emergency
service location.
Gerald T. Mackulak, Ph.D.
Simulation methodology, simulation output
analysis, automated production systems,
material handling design and analysis.
John W. Fowler, Ph.D.
Deterministic scheduling, discrete event
simulation methodology, semiconductor
manufacturing systems analysis, applied
operations research.
Douglas C. Montgomery, Ph.D.
Statistical design of experiments,
optimization and response surface
methodology, empirical stochastic modeling
and industrial statistics.
Esma S. Gel, Ph.D.
Applied probability, stochastic processes,
queuing theory, stochastic modeling and
control of manufacturing systems.
Rong Pan, Ph.D.
Industrial statistics, reliability analysis and
time series modeling.
George C. Runger, Ph.D.
Statistical learning, process control and data
mining for massive, multivariate data sets
with numerous-discipline applications.
Dan L. Shunk, Ph.D.
Agile, enterprise and CIM systems, group
technology, planning systems, economics
of computer-integrated manufacturing,
strategy and strategic role of technology.
J. René Villalobos, Ph.D.
Manufacturing systems, automated visual
inspection, real time quality control and
intelligent manufacturing systems.
Teresa Wu, Ph.D.
Information systems, supply chain
management, multi-agent systems, data
mining, Petri nets and Kalman filtering.
Nong Ye, Ph.D.
Information and systems assurance, security
and dependability of computer and network
systems, data mining and modeling, systems
engineering and management.
Muhong Zhang, Ph.D.
Integer programming, robust optimization,
computational optimization, and network
optimization.
emeritus faculty
James E. Bailey
David Bedworth
Jeffery K. Cochran
Arthur G. Dean
Department of Industrial Engineering
Ira A. Fulton School of Engineering
Arizona State University
P.O. Box 875906
Tempe, AZ 85287-5906
Phone: (480) 965-3185
Fax: (480) 965-8692
www.ie.fulton.asu.edu
Charles Elliott
Norma Hubele
J. Bert Keats
William C. Moor
Richard L. Smith
William R. Uttal
Philip M. Wolfe
Hewitt H..Young