industrial engineering arizona state...

2007-2008 Annual Report

Industrial EngineeringArizona State University

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

ProductionSystems

& Logistics

Operations Research

Information &

Management Systems

Industrial Statistics

IE@ASU Research at a Glance

* ASU IE Faculty Ranked 4th in scholarly productivity by The Chronicle of Higher Education (details on page 3)

Welcome 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.

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,

deployment, support, and r e t i r e m e n t 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.

−Dr.RonaldG.Askin,Chair

Degrees Awarded 2007-2008

Bachelors Masters Doctoral

46 51 13

Enrollment Fall 2007

Bachelors Masters Doctoral

153 95 79

Industrial Engineering 2007-2008 Annual Report

Arizona State University Industrial Engineering2

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 control, applications of

linear models, and time-series 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 CarbideCorporation and Eli Lily and Company, and beena 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

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.

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 awardrecognizes young IIE members who have demonstrated leadership and professionalism in industrial engineering education.

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.

Faculty News, Honors & Awards

Montgomery recieves international engineering award

Esma Gel receives top industrial engineering award

Dr. Douglas MontgomeryDr. Esma S. Gel

Writer: Joe Kullman

h o n o r s

Faculty Honors & Awards 2007-2008 3

George Runger was awarded the 2008 IIE Transactions Quality & Reliability Engineering Best Application Paper award; Runger is one of the authors of “Multivariate Statistical Process Control withArtificial Contrasts,”published in IIE Transactions: Special Issue on Data Mining in 2007.

In addition to his best paper award, Runger was a contributortoArizonaHealthQuery:ACommunity-University 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.

Left to right: Dr. George Runger,

Dr. J. René Villalobos

Villalobos featured in top 100J. René Villalobos was featured in Revista Poder y Dinero, one of the most prestigious financial magazines in Mex-ico 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 andfromuniversitiessuchasHarvardandColumbia.

George Runger awarded for IIE top applied IE research paper & shared ASU President’s Medal

Teaching accoladesEach year, the Ira A. Fulton School of Engineering rec-ognizes its top teaching faculty. Three of the industrial engineeringfaculty–LindaChattin,lecturer,andpro-fessors George Runger and Dan Shunk–were recog-nized in the top 5% of best teachers in the school.

Top faculty are selected in a process that starts by collecting student nominations, which are then evaluatedby theQualityof InstructionCommit-tee. The top professors are then ranked according to the influence they had on their students. Indus-trial engineering faculty are regularly recognized as excelling in teaching evaluations.

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.

Study ranks IE@ASU No. 4


2007-2008 Sponsored ResearchProduction Systems & Logistics

“BannerThroughput Collaborative: OperationsResearch,” Banner Health,JefferyCochran

“Banner Health/ASU Partnership for ED Patient Safety,” Banner Health,JefferyCochran

“ASAP Customization,” Advanced Micro Devices, John Fowler, Gerald Mackulak

“TheSRCFellowship,”SRC, John Fowler, Gerald Mackulak

“Scheduling Assembly and Test Facilities,” Intel, John Fowler, Ron Askin

“GOALI Collaborative Research: MatchingDemand and Supply through Price and Lead Time Decisions,” NSF-ENG Civil, Mechanical and Manufacturing Innovation (CMMI), Esma Gel

“Multi-Product Cycle Time & ThroughputEvaluation,” SRC, Gerald Mackulak, John Fowler

“US-Mexico Partnership on Education & Technology Transfer for the Aerospace Industry,” USAID, Rene Villalobos, John Fowler, Esma Gel

“Arizona State University Affiliation with the Center forEngineeringLogistics&Distribution(CELDi),”NSF, Rene Villalobos, Ron Askin, Esma Gel

“Intelligent Food Defense Systems for International SupplyChains:TheCaseofMexicoFreshProduceto the U.S.,” Department of Homeland Security, Rene Villalobos, George Runger

“CELDi Membership: Forecast and CapacityPlanning for Nogales’ Ports of Entry (Nogales POEs Traffic Study),” ADOT Research Center, Rene Villalobos

Operations Research

“Predicting and Prescribing Human Decision MakingUnderUncertainandComplexScenarios,”AFOSR, Ronald Askin

“Multi-Product Cycle Time & ThroughputEvaluation via Simulation on Demand,” SRC, John Fowler, Teresa Wu

“Collaborative Research: Developing andEngineering Virtual Organization for Discrete-Event logistics Systems,” NSF, John Fowler, Teresa Wu

“Collaborative Research: Optimization of theDesign & Operation Surgery Delivery Systems,” NSF, John Fowler

“Customer Relationship Management at TycoElectronics,” Tyco Electronics, John Fowler“Factory Capacity Allocation Solver for Rapidwithin Shift Re-Planning,” Intel, John Fowler

“MayoClinicCenterforClinicalandTranslationalResearch,” Mayo Foundation, John Fowler

“EPNES: Integrated MEMS & Advanced Technologies for the Next Generation Power Distribution System,” NSF, 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

“Distributed Decision Support Framework for AdaptiveSupplyChains,”IBM, John Fowler, Teresa Wu

“CAREER:Design&ImplementationofaVirtualProduct Development Environment,” NSF, Teresa Wu

“FabricationEnvironmentallyConscious(Benign)Manufacturing into Engineering Education,” UTEP, Teresa Wu, Rong Pan

“Models of Quality of Service and Quality of Information Assurance Towards Their Dynamic Adaptation,” DOD-Air Force Research Labs, Nong Ye

“SoD: Design of Service-Based Software Systems with QoS Monitoring and Adaptation,” NSF, Nong

Ye

Industrial Statistics

“Regression-Based Quality Improvement in Complex Systems with Consideration of DataUncertainty,” NSF, Jing Li

“Advanced Techniques in Design of Experiments for Computational and Physical MultivariateExperiments,” NASA, Douglas Montgomery

“Economical Concrete Mix Designs UtilizingBlended Cements, Performance-BasedSpecifications, and Rational Pay Factors,” ADOT Research Center, Douglas Montgomery, ConnieBorror

“An Interdepartmental Computing Environmentfor Statistical Research,” NSF, Douglas Montgomery,GeorgeRunger,ConnieBorror,andfaculty across ASU campus

“Collaborative Research: Hierarchical Modelingof 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 UsingComputation-IntensiveStatisticalMethods,”NSF, Rong Pan

“Collaborative Research: Blind Discoveryof Variation Sources for Visualization by Multidisciplinary Teams,” NSF, George Runger

“Self-Learning of Decision Rules for Process Control,”NSF, George Runger

“Credit RiskAnalytics,” Desert Schools Fed Credit Union, George Runger

“Data Mining Pilot on Intel Factory Data,” Intel, George Runger

“IntegrationofHealthOutcomesInformation−APartnership with Arizona Department of Environmental Quality,” ADEQ, George Runger

“FeatureSelectionwithEnsemblesforComplexSystems,” NSF, George Runger

Engineering Education

“Collaborative Interdisciplinary ResearchCommunity (CIRC),” NSF, Mary Anderson-Rowland

“CollaborativeResearch: Maricopa EngineeringTransition Scholars (METS),” NSF, Mary Anderson-Rowland

“Collaborative Interdisciplinary ResearchCommunity Maricopa Engineering TransitionScholars (CIRC/METS),”NSF, Mary Anderson-Rowland

“NACME Scholars Program,” NACME, Mary Anderson-Rowland

“Academic & Professional Development for Upper-Division Computer Science, Engineeringand Mathematics Students,” NSF, Mary Anderson-Rowland

“Academic & Professional Development for Lower-Division Computer Science, Engineeringand Mathematics Students,” NSF, Mary Anderson-Rowland

Project Title, Sponsor, P.I.(s)

Ph.D. Degrees Advised 5

2007-2008 Ph.D. Degrees Granted

Summer 2007

Jing HuChange Detection with Supervised LearningAdvisor: George RungerPlacement: SRP

Fall 2007

Napatkamon AyutyanontStatistical Characteristics and Models of Cyber Attack and Norm Data for Cyber Attack DetectionAdvisor: Nong Ye, Randall EubankPlacement: Banner Health Alzheimer Institute

Sandipan GangulyCompromise Based Design: A Penalty Function Approach to Distributed and Collaborative Optimization in DesignAdvisor: Teresa Wu, Ahmet KehaPlacement: Expedia.com

Darshit B. ParmarMitigating Supply Chain Disruption Risk Using Sense and Respond FrameworkAdvisor: Philip Wolfe, Teresa WuPlacement: IBM

Yang SunStrategic and Operational Product Allocation in Semiconductor Supply ChainsAdvisor: Dan Shunk, John FowlerPlacement:CaliforniaStateUniversity,Sacramento

Spring 2008

Jennifer M. BekkiCycle-Time Quantile Estimation with Discrete Event SimulationAdvisor: John Fowler, Gerald MackulakPlacement: Arizona State University, Polytechnic campus

Ozgun B. BekkiDynamic Price and Lead Time Quotation StrategiesAdvisor: Esma GelPlacement: independent contractor

MichaelChiaramonteCompetitive Nurse Rostering and RerosteringAdvisor:JeffreyCochran,TeresaWuPlacement: U.S. Air Force in Japan

ArifeB.ColakHybrid Algorithms for Combinatorial Optimization ProblemsAdvisor: Ahmet KehaPlacement:CentralNewMexicoCommunityCollege

HugoC.GarciaA Framework for the Self Reconfiguration of Automated Visual Inspection SystemsAdvisor: René VillalobosPlacement: Freescale Semiconductor

EricC.MaassModeling the on Time Delivery and Inventory for Semiconductor Supply ChainsAdvisor: John Fowler, Murat KulahciPlacement: Motorola

Myrta R. SigufuentesEvaluation and Construction of Optimal Experimental Designs for Fitting Response Surface ModelsAdvisor:DouglasMontgomery,ConnieBorrorPlacement:TecnológicodeMonterrey,HermosilloCampus

Hugo Garcia, Ph.D., at Spring 2008 graduation.


Bioinformatics uses and develops techniques from disciplines such as statistics, machine learning,

and data mining, to find solutions to bio-logical problems. Large amounts of data are collected on public health, environ-mental, and genomic information. When that data is analyzed with modern ana-lytical methods, the new knowledge can point to causes and aid in prevention or treatment. To solve such problems in bio-informatics, Dr. George Runger is team-ing up with researchers across Arizona State University and the greater Arizona community. Applications of data mining and statistics in bioinformatics are al-ready 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 broad-spectrum 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.”

BioInformatics Methodologies

We discovered statistically significant relationships between air quality and asthma incidents in children.

Research contributors to Arizona HealthQuery : A Community-University Partnership project, recognized with the President’s Medal for Social Embeddedness.

Research: Industrial Statistics 7

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),theASUCenterforHealthInformation 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.”

Extensive health data from CHIRand ADHS provided information on thousands of asthma incidents. This data needed to be linked to environmental air quality data from multiple sensors with important spatial and temporal components. Because asthma has a strong seasonal component, the case-crossover method and other analytical tools to control for long term trends, seasonal effects, epidemics, and other covariates that change slowly with time were used. Nuttha Lurponglukana, an industrial engineering doctoral student

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 datapartners.” The work also led to development of new bioinformatics tools for these types of analyses.

New bioinformatics tools are also an aid in genomic research. The research team from the Department of Industrial Engineering, is collaborating with the Center forEcogenomics, Biodesign Institute at Arizona State University to help investigate the inherent variety of cells and relationships with diseases such as cancer. One research endeavor in the center is the development of single-cell imaging technologies to measure oxygen consumption rates, because of the strong correlation with cell function.

New bioinformatics tools are being developed to study the relationships between oxygen consumption rate,

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 abundance or cell condition that could lead to relevant information to understand diseases. These capabilities will be used to detect features important either individually or involved in interactions.

Doctoral student researcher, Wandaliz Torres-Garcia, explained that, “Despite the increasing molecular knowledge and the technological advances to gather data from biological processes, there remain areas for innovation in data analysis to achieve suitable biological interpretations. Cell pathwaysare still unclear for many diseases and understanding is critical for successful treatments.”

Optimization 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

Robust Optimization


The advantage of [using robust optimization] is that the problem size will not increase dramatically...and will achieve a high quality decision.

Research: Operations Research 9

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 high-quality 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.

“Currently, I am working on the

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.”

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 can be used to schedule shipment

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.

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 ofSciences; and a Bachelor’s degree in Applied Mathematics from Beijing University of ChemicalTechnology.

Dr. Muhong Zhang


Dr. 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

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 5yearsatSEMATECH,anR&Dconsortiumofsemiconductor 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

Schedulingfrom semiconductors to hospitals

Dr. John Fowler

Research: Production Systems & Logistics 11

of researchers, including ASU colleagues Professors George Runger and Esma Gel, Professor Mason, and three colleagues from German institutions, on a proposaltotheFactoryOperationsResearchCenter,fundedbySRCandInternationalSematech,entitled“Scheduling of Semiconductor Wafer Fabrication Facilities.” Over two years, the PIs worked together effectively to:

•Developviableshiftingbottleneck-basedwaferfabscheduling and rescheduling methodologies;• Develop and test wafer fab-specific subproblemsolution procedures for parallel machines requiring auxiliary resources (steppers needing reticles), batch processing machines (diffusion ovens), and machines characterized by sequence-dependent setups (implanters);•Investigatetheutilityofstatisticaloperationscontrolin determining appropriate rescheduling “triggers,” such as deviation from expected job completion time; and•CreateanAutoSchedAP-basedtestingenvironmentto evaluate scheduling approaches in a dynamic, simulation-based environment in order to accommodate real-world fab models.

Experimental results demonstrated the efficacy of scheduling wafer fabs to maximize delivery performance of customer orders in an acceptable amount of computation time. The goal of the experimentation was to find scheduler parameters that maximize on-time delivery performance of orders from customers of varying importance/priority (i.e., total weighted tardiness or TWT) while running at a bottleneck utilization of 95%. The TWT results of the scheduler were compared to those obtained using classical dispatching approaches like: first in, first out (FIFO); earliest due date (EDD);apparenttardinesscostwithsetups(ATCS);criticalratio(CR);andoperationalduedate(ODD).The new scheduler TWT results were between 1% and 25% of the corresponding best dispatching results. In summary, the results demonstrated that a deterministic scheduling-based wafer fab scheduling system has the potential to improve the on time delivery performance of wafer fabs without loss of throughput. This research has motivated several commercial software companies to begin to develop deterministic scheduling systems.

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 simulationmodel of an outpatient procedure center (OPC).Through the use of the model, they demonstrated that how surgeries are scheduled has an impact on the competing objectives of mean patient waiting time andtheamountofovertimeoftheOPC.Inparticular,they found that arrival time schedules substantially influence expected overtime and patient waiting time, while surgery allocation and sequencing heuristics have a smaller effect. Furthermore, they found that surgery mix on a particular day is an important factor affecting performance measures, indicating that the optimization of daily surgical mix may be a promising opportunity for improving scheduling efficiency in an OPC.Inaddition,themodeldevelopedfortheOPChas become the starting point for a model that is being used to help design a new outpatient procedure center. In the continuation of their National Science Foundation projects (DMI-0620573 (Denton) and DMI-0620504 (Fowler)), they will continue to use themodeltostudyhowtoimproveOPCoperations.

In addition to the project described above, ASU IE Ph.D. student Qing Li and Dr. Fowler have worked with colleagues at ASU to improve the capacity planning and day-to-day scheduling of patients for cardiac catheterization procedures. Based on an in-depth study of a major heathcare facility in Arizona, they developed a good understanding of the scheduling problem. By block-scheduling, an initial schedule is generated and then adjusted by a real-time scheduling algorithm. The decision makers can trade-off between multi-objectives and make decisions depending on the importance of different objectives in the healthcare, i.e. patient waiting, facility utilization and staff overtime. The approach was shown via simulation to improve the performance of the scheduling in all measurements. The approach has been implemented in the healthcare facility and shown improvements in the pilot study.


Created 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 modernenterprisesiscalledCollaborativeProductDevelopment (CPD), where partnerships aredeveloped in an effort to improve product quality, and reduce manufacturing costs and production time.“CPDispressinghardto…formavirtualenterprise, in which partners collaboratively respond to the changes of customer demand in a swift manner,” says Dr. Wu.

AnimportantcomponentofasuccessfulCPDis 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 CPDistheeffectivedecisionsupportsystem,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

Design Collaboration

Dr. Teresa Wu

Research: Information & Management Systems 13

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,somechallengingquestionsremainunanswered. 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 pre-qualify 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 pre-qualification, 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.WuisworkingonCPDwithresearchersacrossthenation.CollaboratorsincludeTomThurmanandM.C.JothiofRockwellCollins;James Andary of Nasa Goodard Space Flight Center;KemperLewisoftheUniversityofNew 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.


R e g e n t s ’

Douglas 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 TechnologyandisthecurrentlyoneoftheChiefEditorsofQuality & 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.

Chung,P.J.,Goldfarb,H.B.,andMontgomery,D.C.“Optimal Designs for Mixture-Process Experiments withControlandNoiseFactors,”Journal of Quality Technology, Vol. 39, No. 3, pp. 179-190, 2007.

Holcomb,D.R.,Montgomery,D.C., andCarlyle,W.M. “The use of Supersaturated Experiments in Turbine Engine Development,” Quality Engineering, Vol. 19, No. 1, pp. 17-27, 2007.

Jearkpaporn,D.,Borror,C.M.,Runger,G.C.,andMontgomery,D.C.“ProcessMonitoringforMeanShifts for Multiple Stage Processes,” International Journal of Production Research, Vol. 45, No. 23, pp. 5547-5570, 2007.

Lawson, C. and Montgomery, D.C. “A LogisticRegression 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.,andFowler, 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.,andMontgomery,D.C.“Follow-up Designs to Resolve Confounding inSplit-Plot Experiments,” Journal of Quality Technology, Vol. 40 (2), 2008, pp. 154-166.

Selected Publications

Douglas MontgomeryRegents’ ProfessorCo-Director,ExecutiveCommitteeonStatisticsPh.D., 1969, Virginia Polytechnic Institute and State UniversityStatistical design of experiments, optimization and response surface methodology, empirical stochastic modeling and industrial statisticsQuality and Reliability Engineering Laboratory (Q&RE lab)

Regents’ Professor

Leadership ActivitiesEditor, 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.

Professors 15

P r o f e s s o r s

RonaldG.AskinisaProfessorandDepartmentChairof 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 SocietyofManufacturingEngineers(SME).HeispastChairoftheCouncilofFellowsforIIEandcurrentyservesontheIIE Board of Trustees.

Other awards he has received include the IIE Transactions on Design and Manufacturing Best Paper Award (twice as co-author), 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.

Ronald AskinProfessorandChairPh.D., 1979, Georgia Institute of TechnologyDesign and operation of discrete manufacturing systems, supply chain logistics, decision analysis, applied operations research, facilities planning, industrial statistics and applied optimization

Professors

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,2008,6pages.

Chen, J. andAskin, R. G. “Project Selectionand 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 andControl,” Proceedings of the 19th International Conference on Production Research, Valparaiso,Chile,2007,6pages.

Krishnan, S., and Askin, R.G. “Effect of Information Sharing and Control Strategieson Supply Chain Performance,” International Journal of Simulation and Process Modeling, 3/4, 2006, pp. 175-187.

Askin, R. G. and Chen, J. “Dynamic TaskAssignment 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.


Leadership ActivitiesEditorial Board, International Journal of Industrial and Systems Engineering; Special Issue Co-Editor, International Journal of Production Economics; Board of Trustees, Institute of Industrial Engineers.


John FowlerProfessorPh.D., 1990, Texas A&M UniversityDeterministic scheduling, discrete event simulation methodol-ogy, semiconductor manufacturing systems analysis, healthcare systems analysis and applied operations researchModeling And Analysis For Seminiconductor Manufacturing Laboratory (MASM lab): ie.fulton.asu.edu/research/masm-lab

Fowler, J.W., Wirojanagud, P., and Gel, E.S. “Heuristics for Workforce 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.,andRastogi,A.P.“Enterprise-Wide 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., andHorng, S.M.“EvaluatingAPosterioriSolutionTechniquesforBi-CriteriaParallelMachine Scheduling Problems,” Journal of Scheduling, Vol. 8, No. 1, pp. 75-96, 2005.

Park,S.,Fowler,J.W.,Mackulak,G.T.,Keats,J.B.,andCarlyle,W.M.“D-Optimal Sequential Experiments for Generating a Simulation-BasedCycleTime-ThroughputCurve,”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., InternationalSEMATECH, as well as by several leading semiconductormanufacturers. 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 withtheMayoClinictodevelop“ACurriculumfortheScienceof 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,isTreasurerofOmegaRho(theIEHonorSociety),andistheINFORMSVicePresidentforChapters/Fore.Hewasco-ProgramChairofthe2008IndustrialEngineeringResearchConferenceandProgramChairforthe2008WinterSimulationConference.

J

Leadership ActivitiesArea Editor–Manufacturing, SCS Transactions on Simulation; Area Editor–Planning & Scheduling, Computers and Industrial Engineering; Associate Editor, IEEE Transactions on Electronics Packaging Manufacturing; AssociateEditor–FactoryModelingandControl, IEEE Transactions on Semiconductor Manufacturing;

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.

Professors 17

Gary 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,DODandtheDOC.Hehasalsoservedasaconsultantto 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.

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 theChair of Industrial Engineering atArizona 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 EngineeringAcademic Department Heads, VP of Technical Societies, Director of the OR Division and President of the Arizona ChapterofIIEaswellastheBrazosValleyChapter(Texas).

ProfessionalserviceincludesasEditorialConsultanttotheNational Research Council for Modeling and Simulationin Manufacturing and Defense Systems Acquisition, 2002; ContributingEditor forMcGraw-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.

Gary HoggProfessorPh.D., 1972, University of Texas at AustinApplied optimization, simulation, manufacturing planning and control

Leadership ActivitiesAssociate Editor–Probabalistic Models, Computers & Industrial Engineering


Hwang, W., Runger, G.C., and Tuv, E. “MultivariateStatistical Process Control with Artificial Contrasts,” IIE Transactions: Special Issue on Data Mining, 39(6), 2007, pp. 659-669. IIE Transactions on Quality and Reliability Engineering Best Application Paper Award 2007.

Berrado,A.andRunger,G.C.“UsingMetarulestoOrganizeand Group Discovered Association Rules,” Data Mining and Knowledge Discovery, 14(3), 2007, pp. 409-431.

Runger,G.C.,Barton,R.R.,DelCastillo,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.,andTuv,E.“TunedArtificialContraststo Detect Signals,” International Journal of Production Research: Special Issue on Control Charts, Vol. 45 (23), 2007, pp.5527-5534.

Jearkpaporn, D., Borror, C.M., Runger, G.C., andMontgomery,D.C.“ProcessMonitoringforMeanShiftsforMultiple Stage Processes,” International Journal of Production Research, Vol. 45 (23), 2007, pp. 5547-5570.

Runger,G.C.,Barton,R.R.,Castillo,E.Del, andWoodall,W.H. “Optimal Monitoring of Multivariate Data for Fault Patterns,” Journal of Quality Technology, Vol. 39, No. 2, pp. 159-162, 2007.

Hu,J.,Runger,G.C.,andTuv,E.“ContributorstoaSignalfromanArtificialContrast,”Informatics in Control, Automation and Robotics II, pp. 71-78, Springer, Netherlands, 2007.


GeorgeC.Runger,Ph.D.,isaProfessorinthe 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.

George RungerProfessorPh.D., 1982, University of MinnesotaStatistical learning, process control, and data mining for massive, multivariate data sets with applications in numerous disciplinesQuality and Reliability Engineering Laboratory (Q&RE lab)

Leadership ActivitiesDepartment Editor, Journal of Quality Technology;Associate Editor, Journal of Mathematical and Management Sciences.

Professors 19

Dan 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 ResearchCenter Director. He is currently serving as theAVNETChairofSupplyNetworkIntegration.

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 oftheComputerAidedSystemsAssociationoftheSociety 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.

Dan ShunkProfessor,AVNETChairPh.D., 1976, Purdue UniversityAgile,enterpriseandCIMsystems,grouptechnology, planning systems, economics of computer-integratedmanufacturing(CIM),strategy and strategic role of technologySupply Network Integration Laboratory (SNIL)

Duarte, B., Fowler, J.W., Knutson, K., Gel, E., and Shunk, D. “ACompactAbstraction ofManufacturingNodes ina Supply Network,” International Journal of Simulation and Process Modeling, Vol. 3, Nos. 3, 2007, pp. 115-126.

Shunk,D.,Carter,J.,Hovis,J.,andTalwar,A.“ElectronicsIndustry 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., andTalwar, A. “TheDrivers 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.,andShunk,D.“AStrategicCapacityAllocation Game in the High-Tech Industry,” INFORMS International Meeting 2007, Puerto Rico, July 8-11, 2007.


Leadership ActivitiesEditorial Board, International Journal of Flexible Automation and Integrated Manufacturing; Editorial Board, International Journal of Logistics; Editorial Board, International Journal of Product Development.


Dr. Ye’s past and current research activities–garnering over $9M external funding and producing seventy-six journal papers, two books, including The 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.

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-normseparation 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. 3069-3083.

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.

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, “Qualityof 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.


Nong YeProfessorPh.D., 1991, Purdue UniversityInformation and systems assurance, data mining and modeling, quality optimization and control of system operationsInformation and Systems Assurance Laboratory: isa.eas.asu.edu

Leadership ActivitiesAssociate 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.

A s s o c i a t eAssociate Professors 21

Mary Anderson-Rowland is an associate professor in the Department of Industrial Engineering in the Ira A. Fulton School of Engineering at ASU. Anderson-

RowlandreceivedherB.A.inmathematicsfromHopeCollegein 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.

Mary Anderson-RowlandAssociate ProfessorPh.D., 1966, University of IowaStatistics and probability for quality control, academic scholar-ship programs for all engineering students with an emphasis on women and underrepresented minority students

Anderson-Rowland, M.R., Bernstein, B.L. and Russo, N.F., “The Doctoral Program in Engineering and Computer Science: Is It theSame 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.,“EncouragingTransferStudentstoPursueaBachelor’sDegreeinEngineeringandComputerScience,” Proceedings of the 2007 American Society for Engineering Education Annual Conference & Exposition, Honolulu, Hawaii, June 2007, 7 pages,CD-ROMandwww.asee.org.

Anderson-Rowland, M.R., “A Comparison ofthe Academic Achievements and Retention Rates ofWomenandMenEngineeringandComputerScience Students in an Academic Scholarship Program Designed for Underrepresented Minority Students,” Proceedings of the 2007 WEPAN Conference, Orlando, Florida, June 2007, 11pages,CD-ROMandwww.wepan.org.


Associate Professors

Leadership Activities2007WEPAN Proceedings Chair; 2006-2008PIC IV Chair, Board of Directors, American Society of Engineering Education; 2005 Women in EngineeringDivisionChair,American Society of Engineering Education; Women in Engineering Recruitment and Retention Expert, National Academy of Engineering.


Esma 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 make-to-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.

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 workerDifferences for Workforce lanning,” International Journal of Production Research, 45(3), 2007, pp. 525-553.

Vardar, C., Gel, E.S., Fowler, J.W. “Aframework for evaluating remote diagnostics investment decisions for semiconductor equipment suppliers,” European Journal of Operational Research, 2007, 180(3), pp. 1411-1426.

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.“Modelinginherentworkerdifferencesfor workforce planning,” International Journal of Production Research, 2007, 45(3), pp. 525-553.

Vardar,C.,Gel,E.S.,Fowler,J.W.“Aframeworkfor 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.,andKim,B. “Quantitative comparison of approximate solution sets for bi-criteria optimization problems,” Decision Sciences, 2003, 34 (1), pp. 63-82.


Esma S. GelAssociate ProfessorPh.D., 1999, Northwestern UniversityApplied probability, stochastic processes, queuing theory, stochastic modeling and control of manufacturing systems

Leadership ActivitiesAssociate Editor, Journal of Flexible Services and Manufacturing


Gerald Mackulak is currently participating in sponsoredresearchfromtheSRC/InternationalSemitech. His collaborative research project is

investigating multi-product cycle time and throughput evaluation via simulation on demand, sponsored by ForceII/SRC.

In previous years, he has participated in sponsored researchfromtheSemiconductorResearchCorporation,AnteonCorporation,Asyst,NSF,PRIAutomation, theFederalHighwayCommission,theMcDonnellDouglasCorporation,theHughesMissileSystemsCompany,theInstitute for Manufacturing and Automation Research, theAllied-SignalCorporation,andMotorola.

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 theGeneralChairfortheWinterSimulationConferencein 2011.

Gerald MackulakAssociate ProfessorPh.D., 1979, Purdue UniversitySimulation methodology, simulation output analysis, automated production systems, material handling design and analysis

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-baseddomain 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 andPreciseCycleTime-ThroughputCurves,”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 ActivitiesAssociate Editor, Transactions of the Society for Modeling and Simulation International; Editorial Board, International Journal of Simulation and Process Modeling; General Chair2011,WinterSimulationConference.


René 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 PackardElectricandaprojectengineerforRenaultCompany.Sponsorsof 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 DissertationAwardanda1995NSFCareerGrant.

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.

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.andAguirre,F.“VectorClassificationof SMD Images,” Journal of Manufacturing Systems, Vol. 22, No. 4, pp. 265-282, 2003.

Villalobos, J.R., Munoz, L., and Gutierrez, M.A. “An Application of Fixed and Adaptive Multivariate SPCCharts for On-line Monitoring ofSMD 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.


J. René VillalobosAssociate ProfessorPh.D., 1991, Texas A&M Universitylogistics, automated quality systems, manufacturing systems and ap-plied operations researchInternational Logistics and Productivity Improvement Laboratory (ILPIL): ilpil.asu.edu


Teresa 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.Herresearchinterestsincludecollaborative product development, supply chain management, distributed decision support and informationsystems.Wu’sCAREERprojectisthe“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).

Teresa WuAssociate ProfessorPh.D., 2001, University of IowaInformation systems, supply chain management, multi-agent systems, data mining, Petri nets, Kalman filteringIntelligent Decision Systems Lab: swag.fulton.asu.edu

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 ProductDevelopment,”ASME Transactions: Journal of Computing and Information Science in Engineering, Vol. 4, No. 4, 2004, pp. 281-293, Dec.

Wu,T.,Ye,N.andZhang,D.W.“ComparisonofDistributedMethods for Resource Allocation,” International Journal of Production Research, Vol. 43, No. 3, 2005, pp. 515-536.

Tseng, T-L., Jothishankar, M.C. andWu, T. “QualityControl Problem in PrintedCircuitManufacturing – aRough Set Based Approach,” Journal of Manufacturing Systems, Vol. 23, No. 1, 2004, pp. 56-72.

Parmar, D., Wu, T., Blackhurst, J. “MMR: An Algorithm forClusteringCategoricalDataUsingRoughSetTheory,”Data and Knowledge Engineering, Vol. 63, Issue 3, Dec. 2007, pp. 879-893.


Leadership ActivitiesEditorial 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.

A s s i s t a n t

Ahmet 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 heu-ristic techniques and scheduling.

Keha has presented papers at the INFORMS National Meetings, International Symposium on Mathematical Programming and In-dustrialEngineeringResearchConferences.Someofthejournalsthat he has published are Operations Research, the European Journal of Operational Research, and Operations Research Letters.

Keha, A.B., deFarias, I.R., and Nemhauser, G.L.“ABranch-and-CutAlgorithmwithoutBinary Variables for Nonconvex Piecewise Linear Optimization,” Operations Research, 2006, 54, pp. 847-858.

Colak,A.B.andKeha,A.B.“Interval-IndexedFormulation Based Heuristics for Single Ma-chine Weighted Tardiness Problem,” Computers and Operations Research, accepted for publica-tion. Balasubramanian, H., Fowler, J.W., Keha, A.B. “Scheduling Interfering Job Sets on Par-allel Machines,” European Journal of Operations Research, accepted for publication. Vielma, P.P., Keha, A.B., and Nemhauser, G.L. “Nonconvex, lower semicontinuous piecewise linear optimization,” Discrete Opti-mization, Volume 5, 2008, pp. 467-488.

Keha, A.B, de Farias, I.R, and Nemhauser, G.L.“ABranch-and-CutAlgorithmwithoutBinary 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 Sched-uling Problems,” Computers and Industrial En-gineering, 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


Ahmet KehaAssistant ProfessorPh.D., 2003, Georgia Institute of TechnologyComputationalandtheoreticalaspectsofintegerprogrammingandcombinatorial optimization, modern heuristics techniques, logistics and schedulingLogistics,OptimizationandControlLaboratory(LOCLab)

Assistant Professors



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.

SherecentlyreceivedanIERCBestPaperawardfor“Causation-BasedT2 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).


Jing LiAssistant ProfessorPh.D., 2007, University of MichiganApplied statistics, process control, data mining, causal modeling and inferenceQuality and Reliability Engineering Laboratory (Q&RE lab)

Jin, R., Li, J., and Shi, J. “Quality Prediction and Control in RollingProcesses 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., andChang,T.S. “On-lineSeam 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.

Lin,G.,Li,J.,Hu,S.J.,andCai,W.“AComputationalResponseSurfaceStudyof3D Aluminum Hemming using Solid-to-Shell 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 ControlusingCausalBayesianNetworks,”IIE (Institute of Industrial Engineers) Transactions, 39 (6), 2007, pp. 681-690.

J

Rong 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

fromShanghai JiaoTongUniversity,China, in 1995; hisM.S.in Industrial Engineering from theCollege of Engineering ofFlorida 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 computation-intensive 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.

Zhao, W., Pan, R., Aron, A. and Mettas, A. “Some Properties of Confidence Bounds on ReliabilityEstimation for Parts under Varying Stresses,” IEEE Transactions on Reliability, 55(1): 7-17, 2006.

Colosimo, B.M., Pan, R. and del Castillo, E. “SetupAdjustment 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.


Rong PanAssistant ProfessorPh.D., 2002, Pennsylvania State UniversityIndustrial statistics, reliability analysis and time series modelingQuality and Reliability Engineering Laboratory (Q&RE lab)

Leadership ActivitiesAssociate Editor, Journal of Quality Technology



Muhong 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., and Atamtürk, A. “The Flow Set with Partial Order,” forthcoming in Mathematics of Operations Research, 2008.

Zhang, M., and Atamtürk, A. “Two-Stage Robust Network Flow and Design for Demand Uncertainty,” Operations Research, 2007, Vol. 55, pp.662-673.

Zhang, M. “The Rubust 0-1 Knapsack Polyhedron,” INFORMS Annual Meeting, Seattle, WA, Nov. 2007.

Atamtürk, A., and Zhang, M. “Two-Stage 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.


Muhong ZhangAssistant ProfessorPh.D.,2006,UniversityofCalifornia,BerkeleyInteger programming, robust optimization, computational optimization, and network optimization

Arizona State University Industrial Engineering

v i s i t i n g

Burak 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 ProgrammingApproximationstoNetworkCapacityControlProblem with Buy-ups,” Journal of Revenue and Pricing Management, 7(1), 2008, pp. 61-84.

Büke, B., Ercil, A., and Oden, C. “Combining implicitpolynomials and geometric features for hand recognition,” Pattern Recognition Letters, 24(13), 2003, pp. 2145-2152.

Alla 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.“Bayesiannetworks.”InFloudas,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.

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.

Alla KammerdinerVisiting Assistant Professor

Burak BükeVisiting Assistant Professor

30

2007 Publications 31

Refereed Journal ArticlesArmbruster, 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.,“Evolutionof Operations Management,” Management Research News, Vol. 30, No. 11, pp. 843-871, 2007.

Berrado,A.,andRunger,C.G.,“UsingMetarulestoOrganize and Group Discovered Association Rules,” Data Mining and Knowledge Discovery, Vol. 14, No. 3, pp. 409-431, 2007.

Chung,P.J.,Goldfarb,H.B.,andMontgomery,D.C.,“OptimalDesignsforMixture-ProcessExperimentswithControlandNoiseFactors”,Journal of Quality Technology, Vol. 39, No. 3, pp. 179-190, 2007.

Duarte, B., Fowler, J.W., Knutson, K., Gel, E.,andShunk,D.,“ACompactAbstractionofManufacturing 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., andGel,E.S.,“ComparisonofFrameworksforNext Generation Multiphase Flow Solver, MFIX: A Group Decision-Making Exercise,” Concurrency and Computation: Practice and Experience, Vol. 19, pp. 609-624, 2007.

Gel, E. S., Hopp, W. J., and Van Oyen, M. P., “Hierarchical cross-training in Work-In-Process-ConstrainedEnvironments,” IIE Transactions, 39(2), pp. 125-143, 2007.

Holcomb,D.R.,Montgomery,D.C.,andCarlyle,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.,andTuv,E.,“TunedArtificialContraststoDetectSignals,”International Journal of Production Research: Special Issue on Control Charts, Vol. 45, No. 23, pp. 5527 – 5534, 2007.

Hwang,W.,Runger,G.C.,andTuv,E.,“MultivariateStatisticalProcessControlwithArtificialContrasts,”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.

Jearkpaporn,D.,Borror,C.M.,Runger,G.C.,andMontgomery,D.C.,“ProcessMonitoringforMeanShifts for Multiple Stage Processes”, International Journal of Production Research, Vol. 45, No. 23, pp. 5547-5570, 2007.

Jeong, I-J., Leon, J.V., Jorge, V., and Villalobos, J. R., “Integrated decision support system for diagnosis, maintenance planning and scheduling

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 andControlinRollingProcessesusingLogisticRegression,” Transactions of NAMRI/SME (North American Manufacturing Research Institution of Society of Manufacturing Engineers), 35, pp. 113-120, 2007.

Kwon,Y-J.,Wu,T.,“CognitiveUnderstandingofRemote 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.andMontgomery,D.C.,“ALogisticRegression 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 ObservationalDataforProcessControlusingCausalBayesianNetworks,”IIE (Institute of Industrial Engineers) Transactions, 39 (6), pp. 681 – 690, 2007.

Li,J.,Shi,J.,andChang,T.S.,“On-lineSeamDetection 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.,andCai,W.,“AComputationalResponseSurfaceStudyof3DAluminum 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.

Mönch, L., Schabacker, R., Pabst, D., and Fowler, J.W., “Genetic Algorithm-Based Subproblem Solution Procedures for a Modified Shifting BottleneckHeuristicforComplexJobShops”,European Journal of Operational Research, Vol. 177, No. 3, pp. 2100-2118, 2007.

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 AlgorithmforClusteringCategoricalDataUsingRough Set Theory,” Data and Knowledge Engineering, Vol. 63, Issue 3, pp. 879-893, Dec. 2007.

Perry,L.A.,Montgomery,D.C.,andFowler,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,andWoodall, 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.andTalwar,A.,“TheDrivers 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.,andFowler,J.W.,“AFramework 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.,“ModelingInherentWorkerDifferencesfor Workforce Planning”, International Journal of Production Research, Vol. 45, No. 3, pp. 525 – 553, 2007.

Wu, T., Blackhurst, J., and O’Grady, P., “A MethodologyforSupplyChainDisruptionAnalysis,”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. 174-192, 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-NormSeparationFordetecting Attack-Induced Quality Problems on

2007 Publications


ComputersandNetworks,”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. 3069-3083, 2007.

Zhang, M., and AtamtÄurk, A., “Two-Stage Robust Network Flow and Design for Demand Uncertainty,” Operations Research, Vol. 55, pp. 662-673, 2007.

Anderson-Rowland,M.R.,“AComparisonoftheAcademic Achievements and Retention Rates of WomenandMenEngineeringandComputerScience Students in an Academic Scholarship Program Designed for Underrepresented Minority Students,” Proceedings of the 2007 WEPAN Conference, Orlando,Florida,June2007,11pages,CD-ROM,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 EngineeringandComputerScience,”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 andComputerScience:IsIttheSameforWomenand Men?” Proceedings of the 2007 WEPAN Conference, Orlando,Florida,June2007,14pages,CD-ROM,and www.wepan.org.

Anderson-Rowland,M.R.,&Culley,P.I.,“HelpingLower 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.,“AThreeYearEvaluationofaNACMEProgram,”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 CorrelationbetweenGPAandPercentEffortontheGuaranteed 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.,“EncouragingTransferStudentsToPursuea

Bachelor’sDegreeinEngineeringandComputerScience,” Proceedings of the 2007 American Society for Engineering Education Annual Conference & Exposition, Honolulu, Hawaii, June 2007, 7 pages, www.asee.org.

Askin, R. G., D. Pabst, Pew, M., and Sun, Y., “Using Real Time Information in Operational Planning andControl,”Proceedings of the 19th International Conference on Production Research,Valparaiso,Chile,2007, 6 pages.

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,DC,Dec.9-12,2007, pp. 1722-1728.

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.

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.

Habla,C.,Mönch,L.,Pfund,M.E.,andFowler,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.,andTuv,E.,“ContributorstoaSignalfromanArtificialContrast,”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-CriteriaEvaluationofanOutpatientProcedureCenterviaSimulation,”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.,andYe,N.,Chapter16,“Intrusiondetectionand 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.

Pan, R., Solis, A. and Paul, B., “Demand-Supply InteractionandProductionCapacityPlanningforShortLifeCycleProducts,”Proceedings of the 36th Annual Meeting of the Western Decision Sciences Institute, 2007.

Ye,N.,Lai,Y.C.,andFarley,T,“QualityofService Assurance for Dependable Information Infrastructure,” Information Security Research: New Methods for Protecting Against Cyber Threats, Wiley, Chapter1.2.1,pp.53-79,Indianapolis,Indiana,2007.

Ye,N.,andZhao,L.,“OnsetofTrafficCongestioninComplexNetworks,”Information Security Research: New Methods for Protecting Against Cyber Threats. Wiley, Chapter1.2.2,pp.80-87,Indianapolis,Indiana,2007.

Montgomery,D.C.,andRunger,G.Applied Statistics and Probability for Engineers, 4th Edition. John Wiley & Sons. 768 pages, New York, 2007.

Montgomery,D.C.,Runger,G.C.,andHubele,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.

Anderson-Rowland,M.R.,“AComparisonoftheAcademic Achievements and Retention Rates of WomenandMenEngineeringandComputerScience. Students in an Academic Scholarship Program Designed for Underrepresented Minority Students,” Invited Presentation: NACME National Conference,BestPractices,Denver,CO,7/29/07.

Anderson-Rowland,M.R.,“AComparisonoftheAcademic Achievements and Retention Rates of WomenandMenEngineeringandComputerScience Students in an Academic Scholarship Program Designed for Underrepresented Minority Students,” Proceedings of the 2007 WEPAN Conference, Orlando,Florida,June2007,11pages,CD-ROM,and www.wepan.org.

Anderson-Rowland, M.R., Invited Presentation: “Academic Leadership,” Society of Women 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 ComputerScience:IsIttheSameforWomenandMen?” Proceedings of the 2007 WEPAN Conference,

ConferenceProceedings,BookChapters

ConferenceProceedings

Books Authored

Orlando,Florida,June2007,14pages,CD-ROM, 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 ComputerScience,”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.,“AThreeYearEvaluationofaNACMEProgram,” 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.,“EncouragingTransferStudentsToPursuea Bachelor’s Degree in Engineering and ComputerScience,”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 OperatingCells,”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 Anderson-Rowland, 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,August2007.

Borisov, A., Runger G., and Eugene T., “ContributorDiagnosticsforProcessMonitorsfromArtificialContrasts,”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 OptimizationforQuasi-ConcavePreferenceFunctions,” 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.

Fowler, J.W., Sun, Y., and Shunk, D., “A StrategicCapacityAllocationGameintheHigh-Tech Industry,” INFORMS International Meeting 2007, Puerto Rico, July 8-11, 2007.

Ganguly, S., Keha, A., and Wu, T., “Penalty FunctionApproachforCompromiseMechanismsinDistributedCollaborativeDesign Optimization,” INFORMS National Meeting, Seattle, November 2007.

Garcia,H,C.,andVillalobos,J.R.,“ANovelFeature Selection Method for the Quadratic Discriminant Function,” INFORMS Annual Meeting, Seattle, WA, November 4-7, 2007.

García,H.C.,andVillalobos,J.R.,“AReconfigurable 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.,andPan,R.,“GreenSupplyChainManagementanditsOpportunities,” presented at the Southeast INFORMS, 2007.

Hu,J.,Runger,G.C.,andTuv,E.,“Self-Learning of Decision Rules for Statistical ProcessControl,”theNational Science Foundation CMMI Conference, Knoxville, TN, 2007.

Keha, A., “Using Integer Programming for Solving Problems in Information Theory,” Invited Talk at the INFORMS National Meeting, Seattle, November 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.

Keha,A.,Colak,A.,andHaralson,S.,“Improving Airline Schedule Planning at Swift Aviation Group,” INFORMS National Meeting, Seattle, November, 2007.

Montgomery,D.C.,InvitedkeynoteAddress,“The Modern Practice of Statistics in Business and Industry,” Swiss Statistics Meeting, Lucerne, Switzerland, 14-16 November, 2007.

Montgomery,D.C.,“AModernFrameworkfor Enterprise Excellence,” Deming Lecture,

presented at the Joint Statistical Meetings, Salt LakeCity,31July,2007.

Montgomery,D.C.,“StatisticsandScience,Business and Industry,” Invited Keynote Presentation at the JMP Users’ Conference,CaryNC,13June2007.

Montgomery,D.C.,“TeachingDOX:SomeAdventures and Lessons Learned,” Invited Plenary Presentation at the ASA Quality and Productivity Research Conference, Santa Fe, New Mexico, 4 June 2007.

Pan,R.,andGamez,H.,“EffectsofCommonCauseFailureontheSystemSubjecttoCompetingRisks,”presentedatthe51st ASA/ASQ Fall Technical Conference, Jacksonville, FL., 2007.

Runger, G., Invited presentation, Intel Corporate Statistical Summit, September, 2007. Runger, G., Invited Presentation: Data Mining Research, Weyerhaeuser Research Meeting, August, 2007, Seattle, WA

Runger,G.C.,andTuv,E.,“FeatureSelectionwithEnsemblesforComplexSystems,”theNational 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 ClassroomGame,”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.

Villalobos, J.R., “Productivity Improvement Opportunities in Industry,” Presentation to industry and students, Texas State University, San Marcos, TX, November 9 2007.

Villalobos, J.R., “The Stock Game,” Presentation to Industrial Engineering students, Texas State University, San Marcos, TX, November 9 2007.

Villalobos, J.R., and Ahumada, O., “Development of Planning Tools for the Supply ChainofFreshProduce,”INFORMS Annual Meeting, Seattle, WA, November 4-7, 2007.

Zhang, M., “The Robust 0-1 Knapsack Polyhedron,” INFORMS Annual Meeting, Seattle, WA. Nov. 2007.

Mary R. Anderson-Rowland, Ph.D.Statistics and probability for quality control, academic scholarship programs for all engineering students with an emphasison 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.

Linda Chattin, Ph.D.Discrete optimization, stochastic processes and probabilistic modeling, emergency service location.

John W. Fowler, Ph.D.Deterministic scheduling, discrete event simulation methodology, semiconductor manufacturing systems analysis, applied operations research.

Esma S. Gel, Ph.D.Applied probability, stochastic processes, queuing theory, stochastic modeling and control of manufacturing systems.

Gary L. Hogg, Ph.D.Applied optimization, simulation, manufacturing planning and control.

Ahmet B. Keha, Ph.D.Computational and theoretical aspects ofinteger programming and combinatorial optimization, modern heuristics techniques, logistics and scheduling.

Jing Li, Ph.D.Applied statistics, process control, data mining, causal modeling and inference.

Gerald T. Mackulak, Ph.D.Simulation methodology, simulation output analysis, automated production systems, material handling design and analysis.

Douglas C. Montgomery, Ph.D.Statistical design of experiments, optimization and response surface methodology, empirical stochastic modeling and industrial statistics.

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 andCIM systems, grouptechnology, 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 andintelligent 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.

ie faculty

Department of Industrial EngineeringIra A. Fulton School of EngineeringArizona State UniversityP.O. Box 875906Tempe, AZ 85287-5906

Phone: (480) 965-3185Fax: (480) 965-8692www.ie.fulton.asu.edu

James E. BaileyDavid BedworthJefferyK.CochranArthur G. Dean

CharlesElliottNorma HubeleJ. Bert KeatsWilliamC.Moor

Richard L. SmithWilliam R. UttalPhilip M. WolfeHewitt H.. Young

emeritus faculty

industrial engineering arizona state...

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