Chair    James M. Tien
Associate Chair and Director, Master’s Programs   Charles J. Malmborg
Director, Doctoral Program   Robert J. Graves
Department Home Page   http://www.rpi.edu/dept/dses/www/

The formation of the Decision Sciences and Engineering Department in 1987 is a prime example of Rensselaer’s ability to anticipate the changing needs of the engineering profession. The department was created to (1) prepare engineers to design, develop, and implement complex decision-making systems and (2) to conduct research that leads to better understanding of how information technology and quantitative analysis and modeling can support individuals, groups, and systems in problem solving and decision making. DSES achieves these objectives by extending and integrating knowledge from the disciplines of industrial engineering, information systems, operations research, mathematical statistics, computational intelligence/biotechnology, and systems engineering.

The Department of Decision Sciences and Engineering systems offers programs in industrial and management engineering, manufacturing systems engineering, and operations research statistics. Curricula in management engineering and/or industrial engineering have been offered since 1933. The interdisciplinary graduate program in operations research and statistics (OR&S) at Rensselaer was established in response to the rapid increase in the use of mathematical models for characterizing systems, understanding operations, and making decisions. Both a master’s and a doctoral program were initiated in 1967. However, in 1988, the department replaced the OR&S Ph.D. with a unique Ph.D. degree in Decision Sciences and Engineering Systems, reflecting the focus of the department. The program in Manufacturing Systems Engineering was inaugurated in the fall of 1992. This program is designed to emphasize modeling, statistical, computer, and management skills as they relate to the process of manufacturing. A common theme throughout these programs is the use of mathematical, statistical, and computational/simulation models to better understand engineering, managerial, operational, and physical processes.

Research and Innovation Initiatives

Manufacturing Systems
Faculty have developed methodologies and procedures for infrastructure and operating systems (e.g., production planning and control, scheduling, and dispatching in flexible manufacturing systems), simulation of production facilities, manufacturing logistics, materials handling engineering, manufacturing facility design, information integration for design and manufacturing, control systems and agile manufacturing concepts for the electronics industry, and methodologies to integrate statistical quality control with computer graphics.

Service Systems
This area concentrates on the application of traditional and evolving industrial and systems engineering methodologies to the design and operation of service systems in both industry and the public sector. Areas of interest include simulation modeling and analysis, distribution and logistics, facilities design, work design, quality assurance, intelligent transportation systems, and engineering economic analysis. Also included is research in the deployment, allocation, and operation of urban service systems using computationally-based decision support methods.

Information Systems
Information and decision support systems have been developed and extensively used for disaster preparedness and management of disasters (e.g., searches for ships lost at sea, earthquakes) and manufacturing enterprises (e.g., manufacturing-driven design and scalable adaptive integration of databases over wide area networks). New theory and methodologies for Internet-based information integration, e-commerce, data mining, and knowledge discovery are being developed. Decision support systems are being developed using a variety of knowledge engineering and computational intelligence tools. Also under development are methods, models, and technologies to aid in the planning and design of distributed information systems, information visualization, and user interfaces.

Mathematical Programming
Research topics include linear, nonlinear, integer, large-scale, multiple-objective, combinatorial, geometric, and stochastic programming. Of particular interest is research on the development and analysis of algorithms, computation, and the integration of uncertainty in optimization.

Statistics and Applied Probability
Research is conducted in the areas of data mining, knowledge discovery, and design of experiments—including optimality, efficiency, and robustness; nonlinear and robust estimation; statistical computing; probability; stochastic processes; queuing theory; reliability; quality control; and forecasting.

Facilitating these research programs are three research centers based directly within the Decision Sciences and Engineering Systems Department. Every department faculty member is involved in one or more of these research centers. In addition, several other faculty in the School of the Engineering, as well as in the other four schools, are also participating in activities conducted in the centers described below:

Electronics Agile Manufacturing Research Institute (EAMRI)
The EAMRI grew out of a federally funded, five-year project focused on agile manufacturing information technologies as a strategy to help the electronics manufacturing industry achieve its goals. Agile manufacturing concepts employ network-based information for supply chain-oriented technologies and organizations, as well as improved communications to help solve design and manufacturing problems. The EAMRI provides a national focus for developing and sharing methods to enable the U.S. electronics industry to adopt agile manufacturing. Experts in electronics design and manufacturing, the EAMRI faculty are associated with engineering, computer science, and management disciplines. The EAMRI’s initial information technology, known as the Virtual Design Environment, has recently received a patent from the U.S. Patent Office.

Center for Services Research and Education (CSRE)
The goal of the CSRE is to enhance our understanding of the services sector and its function, and to educate students and managers seeking careers in the services industry, which accounts for more than three-quarters of the U.S. gross national product. CSRE faculty were one of the first groups to highlight the duality between services and manufacturing; many manufacturing methods are applicable to services systems and can be employed to enhance productivity and competitiveness. The CSRE takes a holistic approach to the multifaceted services sector and brings together experts from engineering, marketing, psychology, economics, and management policy and organization, among others. Experts examine the common elements that characterize all aspects of the services sector and develop generic principles that apply across the wide spectrum of services.

Rensselaer Statistical Consulting Center (RSCC)
The RSCC provides statistical planning and analysis services to Rensselaer researchers who require them. It also consults with companies and government agencies that require advice on state-of-the-art statistical and probabilistic methods and their applications. In addition, it allows graduate students to apply, in a supervised manner, established and new statistical and probabilistic approaches to real-world problems, and offers general and organization-specific short-term training programs and state-of-the-art courses in statistical methodologies and practices. The Center’s faculty represent a range of statistical expertise, and they have extensive research and consulting experience. These faculty members, together with talented graduate students, provide advice and guidance on the appropriate use of statistical and probabilistic methods, on a consulting or short course basis.

Faculty

Departmental faculty listings are accurate as of the date generated for inclusion in this catalog. For the most up-to-date listing of faculty positions, including end-of-year promotions, please refer to the Faculty Roster section of this catalog, which is current as of the May 2002 Board of Trustees meeting.

Professors
Berg, D.—NAE, Ph.D. (Yale University); management of technological organizations, innovation, policy, robotics, policy issues of research and development in the service sector.
Ecker, J.G.—(Mathematical Sciences) Ph.D. (University of Michigan); mathematical programming, multiobjective programming, geometric programming, mathematical programming applications, ellipsoid algorithms.
Grabowski, M.—Ph.D. (Rensselaer Polytechnic Institute); management information systems, knowledge-based systems, human and organizational error in large-scale systems, impact of information technology on systems and organizations; Research Professor.
Graves, R.J.—Ph.D. (State University of New York at Buffalo); manufacturing systems modeling and analysis, facilities planning and material handling system design, scheduling systems, concurrent engineering and design for manufacture, continuous flow manufacturing systems design, distributed manufacturing concepts, information infrastructure.
Hsu, C.—Ph.D. (Ohio State University); electronic commerce, metadatabase and information systems, enterprise integration and modeling, internet enterprises planning, computerized manufacturing, information visualization, economic evaluation of cyberspace-augmented enterprises.
Hughes, G.—(Economics) Ph.D. (Princeton University); global economics, economics of information technology; Clinical Professor.
List, G.F.—(Civil Engineering) P.E., Ph.D. (University of Pennsylvania); real-time control of transportation network operations; multiobjective routing, scheduling, and fleet sizing; operations planning; hazardous materials logistics.
Malmborg, C.J.—Ph.D. (Georgia Institute of Technology); modeling and analysis of problems in facility design, materials handling, material flow, storage systems, simulation-based optimization methods, manufacturing systems, decision analysis.
Raghavachari, M.—Ph.D. (University of California at Berkeley); statistical inference, quality control, multivariate methods, scheduling problems.
Tien, J.M. — (Electrical, Computer, and Systems Engineering) NAE, Ph.D. (Massachusetts Institute of Technology); systems modeling, queuing theory, public policy and decision analysis, computer performance evaluation, information and decision support systems, expert systems, computational cybernetics.
Wallace, W.A.—Ph.D. (Rensselaer Polytechnic Institute); decision support systems, environmental management modeling process, disaster management.
Willemain, T.R.—Ph.D. (Massachusetts Institute of Technology); probabilistic modeling, data analysis, forecasting.

Associate Professors
Embrechts, M.J.—Ph.D. (Virginia Polytechnic Institute); application of neutral networks and fuzzy logic for manufacturing and process control; image recognition and classification with the aid of neural networks; smart experiments; neural networks for trading and finance; neural networks, fractals, chaos, and wavelets for time-series analysis; data mining and computational intelligence.
Foley, W.J.—P.E., Ph.D. (Rensselaer Polytechnic Institute); engineering design, computer simulation modeling, health applications of operations research, health case policy analysis.
Heragu, S.S. —Ph.D. (University of Manitoba); artificial intelligence, cellular manufacturing, facilities design, intelligent manufacturing systems, materials handling, next-generation factory layout design, production and operations management, operations research, scheduling, storage and warehousing.
Mitchell, J.E.—(Mathematical Sciences) Ph.D. (Cornell University); mathematical programming, integer programming, interior point methods, column generation methods, financial optimization, stochastic programming.
Sullo, P.—Ph.D. (Florida State university); reliability, life testing, statistical quality control, quality management, biostatistics, industrial statistics.

Assistant Professors
Aboul-Seoud, M.—Ph.D. (University of Louisville); reliability engineering, quality control, operations research.
Gupta, A.—Ph.D. (Stanford University); behavioral aspects of optimization and application problems in finance, large-scale problems in decision making, simulation methods and tools for solving large-scale problems, simulation-based optimization.
Taner, M. —Ph.D (North Carolina State University); e-commerce, production scheduling, distributed manufacturing systems.
Yang, Y.—(Cognitive Science) Ph.D. (New York University); cognitive psychology, thinking, reasoning, decision-making, cognitive science.

Adjunct Faculty
Buttridge, J.J.—B.S. (Husson College); safety management, environmental safety and health, occupational safety, hazardous waste management.
Kupferschmid, M.—P.E., Ph.D. (Rensselaer Polytechnic Institute); mathematical programming, algorithm performance evaluation, engineering applications.
Lawrence, C.—Ph.D. (Cornell University); statistical methods for bioinformatics, biometrics, Bayesian statistics, sequential analysis, statistical computing.
Mars, C.M.—B.S. (Rensselaer Polytechnic Institute); industrial safety and hygiene.
Sandhu, D.—Ph.D. (University of Toronto); stochastic models in operations research, complex queuing networks, applications to communication and manufacturing systems.

Affiliated Professors
Desrochers, A.—(Electrical, Computer, and Systems Engineering) Ph.D. (Purdue University); performance modeling of automated manufacturing systems application to Petri nets, transfer lines, manufacturing architectures, database and network transactions, distributed systems.
Grivas, D.—(Civil Engineering) Ph.D. (Purdue University); engineering infrastructure asset management systems, infrastructure databases, applications of fuzzy sets and expert systems, probabilistic modeling, risk analysis, assessment, management.
Kelly, L.J.—(Rensselaer at Hartford) Ph.D. (University of Connecticut); statistics, operations management.
Norsworthy, J.R.—(Lally School of Management and Technology) Ph.D. (University of Virginia); economics of productivity, productivity measurements, industrial economics.
Paulson, A.S.—(Lally School of Management and Technology) Ph.D. (Virginia Polytechnic Institute); risk management, financial models, multivariate statistics, time series and forecasting, survival data analysis.

Affiliated Associate Professors
Bennett, K.—(Mathematical Sciences) Ph.D. (University of Wisconsin); mathematical programming, operations research, artificial intelligence.
Franklin, W.R.—(Electrical, Computer, and Systems Engineering) Ph.D. (Harvard University); computational geometry, graphics, CAD, cartography, parallel algorithms, large databases, expert system verification.
Goldenberg, D.H.—(Lally School of Management and Technology) Ph.D. (University of Florida); derivatives markets, stochastic modeling of prices, options in corporate finance.
Gutierrez-Miravete, E.—(Rensselaer at Hartford) Ph.D. (Massachusetts Institute of Technology); materials processing, transport phenomena, clean technologies, advanced mathematics for applications, numerical computing, mathematical modeling, computer simulation.
Maleyeff, J.—(Rensselaer at Hartford) Ph.D. (University of Massachusetts); statistical quality assurance in manufacturing, administration and health care; computer simulation of operating systems; development of effective teaching methodologies.

Affiliated Assistant Professor
Arnheiter, E.D.—(Rensselaer at Hartford) Ph.D. (University of Massachusetts); Monte Carlo simulation and probabilistic models in quality, modular consortiums, and automotive production models.
Ravichandran, T.—(Lally School of Management and Technology) Ph.D. (Southern Illinois University, Carbondale); management information systems.
Zaki, M.J.—(Computer Sciences) Ph.D. (University of Rochester); design of efficient, scalable, and parallel algorithms for various data mining techniques.

Undergraduate Programs

Objectives of the Undergraduate Curriculum
While the objectives stated in the School of Engineering’s Overview of Undergraduate Programs apply to all departments, achievement of the third objective requires a subset of specific objectives to ensure all graduates have specialized technical knowledge in their chosen fields. In this regard, the Decision Sciences and Engineering Department’s baccalaureate program in Industrial Management and Engineering will ensure that its graduates have the ability to:

• Apply computing in data analysis, modeling, and problem solving.
• Approach problems from a total integrated systems perspective.
• Apply knowledge of manufacturing and service systems in managing people and systems to achieve and maintain the efficient use of resources.

Baccalaureate Programs
The Department of Decision Sciences and Engineering offers a curriculum in Industrial and Management Engineering (IME). The first two years of this curriculum provide a strong foundation in basic science, engineering science, mathematics, and the humanities and social sciences. These two years are oriented toward the quantitative (mathematical) approach. Computer-based technology, including simulation, modeling, and systems design, is emphasized. In the last two years of the program, students concentrate on building expertise in statistics, operations research, manufacturing, and industrial engineering methods and models. Through the appropriate choice of electives, students can focus on their selected areas of interest. Design projects include problems in both manufacturing and service systems, including information and public systems. It is advisable to develop a plan of study leading to the desired degree and concentration by the beginning of the third year.

The IME curriculum seeks to graduate high quality industrial and management engineers and to prepare them for successful 21st century careers. Such careers require graduates have the ability to apply knowledge of mathematics, science, and computing; to analyze and interpret data; and to identify, formulate, and solve problems. They must also understand the impact of engineering solutions in a global and societal context and to approach a problem from a total integrated systems perspective. Additional skills necessary include applying knowledge of manufacturing and service systems in managing people and systems to achieve and maintain the efficient use of resources; communicating effectively and functioning as a leader on multidisciplinary teams; and actively engaging in lifelong learning.

DSES recommends that students declare their intent to major in industrial and management engineering as early as possible in their academic career. Students are also urged to work closely with their assigned faculty advisers to ensure that all degree requirements are satisfied. This curriculum requires a minimum of 125 credit hours and completion of the course requirements shown in the typical four-year program presented below.

1. Students may take any approved four-credit course in science, computer science, or mathematics.
2. Students must select any two of the following approved multidisciplinary electives:
ENGR-1600
ENGR-2090
ENGR-2250
ENGR-2530
ENGR-4050
ENGR-4300
3. This course can be fulfilled by taking a two-credit course from a list of courses published at the start of each semester.
4. Students may select any two of the following courses to satisfy technical elective requirements:
DSES-4200
DSES-4250
DSES-4260
DSES-4810
DSES-4280
5. May be taken in either fall or spring semester.

Electives
The free electives indicated above may be chosen from any academic discipline to broaden the student’s educational background and/or develop greater depth in a selected discipline.

Humanities or Social Sciences Electives
The electives in this area are based on the Institute and School of Engineering requirements. Students are urged to elect humanities and social science sequences through which they will obtain adequate breadth and depth in subject areas. Students desiring minors in Humanities and Social Sciences must consult the school or department in which the courses are offered to obtain further information and specific requirements.

Special Undergraduate Opportunities

Cooperative Education Program
DSES encourages this option, which allows students to gain professional experience as part of the educational program. Additional information on co-op opportunities is included in the Educational Programs and Resources section of this catalog, as well as through the faculty adviser or the Career Development Center.

Graduate Programs

Master’s Programs
The Department of Decision Sciences and Engineering Systems offers two master’s level degrees, both of which can be earned within each of three curricula. These curricula and their individual requirements are described below.

Industrial and Management Engineering Curricula
Students can work toward either the M.S. degree, which requires a project or thesis, or the nonthesis M.Eng. degree. These IME degrees are also available through distance learning. Contact the Office of Professional and Distance Education for course scheduling information.

In general, all applicants to the IME master’s programs must take the Graduate Record Exam (GRE). This requirement is especially important for those requesting financial aid, due to the large number of aid requests. However, for students applying to the M.Eng. program, the GRE may sometimes be waived. In special situations, and with departmental approval, the GMAT may be substituted for the GRE.

All students seeking either of the IME master’s degrees must have completed the following two courses in their undergraduate program at Rensselaer or have had the equivalent course elsewhere:

Applied Operations Research (equivalent to DSES-6960 Systems Modeling and Decision Sciences)
Introduction to Applied Statistics (equivalent to DSES-6110)

Both IME master’s degrees require a minimum of 30 credit hours.

In addition to the above prerequisite courses, a student’s core course work must include:

For students seeking the M.S. degree, three to nine of the 30 credit hours must be in DSES-6990 (Master’s Thesis) or DSES- 6980 (Master’s Project). The thesis or project credits can also count toward the nine-credit-hour (three-course) concentration that the program requires.

While the remainder of the program may be tailored to the student’s interest, the plan of study must include a concentration area. The concentration is a set of three or more courses (or nine credit hours) that reflects a logical progression for developing a base of expertise in an area of study. Concentrations will usually, but not always, include at least one of the core courses listed above. Listed below are several concentration areas and the courses that are applicable to them.

* Students can also satisfy this part of the requirement for the service systems concentration through two application-focused courses approved by the academic adviser. Examples of applications foci include financial services, health systems, transportation, retailing, public systems, quality systems, and marketing.

Although it is possible for a well-prepared student to complete the requirements for the master’s degree in one academic year, it is recommended that the student allow a full calendar year or three academic semesters to complete the program of study.

Manufacturing Systems Engineering (MSE) Curricula

In this curriculum, DSES offers multidisciplinary M.S. and M.Eng. degrees in programs that combine modeling, statistical, computer, design, and management skills focused on the process of manufacturing. The programs provide students with an option to combine courses in DSES with concentration courses in electrical engineering, mechanical engineering, computer science, or management. Alternatively, students can elect to focus the entire plan of study in DSES. Course work concentrations are available in manufacturing systems modeling, manufacturing information systems, manufacturing systems quality, manufacturing processes and technology, and manufacturing systems management.

All applicants to this program are encouraged to take the Graduate Record Exam (GRE). This requirement is especially important for those requesting financial aid, due to the large number of aid requests. The GRE may be waived or the GMAT may be substituted for the GRE for students applying to the M.Eng. program.

All students seeking either of the MSE master’s degrees must have completed the following two courses in their undergraduate program at Rensselaer or have had the equivalent course elsewhere:

Applied Operations Research (equivalent to DSES-6960 Systems Modeling and Decision Sciences)
Introduction to Applied Statistics (equivalent to DSES-6110)

Both MSE master’s degrees require a minimum of 30 credit hours.

In addition to the above prerequisite courses, a student’s core course work must include:

For students seeking the M.S. degree, three to nine of the 30 credit hours must be in DSES-6990 (Master’s Thesis) or DSES- 6980 (Master’s Project). The thesis or project credits can also count toward the nine-credit-hour (three-course) concentration that the program requires.

While the remainder of the program may be tailored to the student’s interest, the plan of study must include a concentration area. The concentration is a set of three or more courses (or nine credit hours) that reflects a logical progression for developing a base of expertise in an area of study. Concentrations will usually, but not always, include at least one of the core courses listed above. Listed below are several concentration areas and the courses that are applicable to them.

For most students, the M.Eng. program of study will also allow for six credits of elective courses.

Operations Research and Statistics Curricula

In this curriculum, DSES offers M.S. and M.Eng. degree programs that combine course work in applied probability and statistics, optimization, modeling, and decision sciences in the scientific application of quantitative tools to support decision making. The scope of the programs includes the formulation, solution, and implementation of mathematical models of decision problems to measure, evaluate, and optimize system performance. Most students can complete either program in 30 credit hours. Any student planning to enroll in the DSES doctoral program should consult with the faculty adviser prior to making course selections.

The diversity of this curriculum’s faculty enables these programs to encompass varied research topics. A student may work simultaneously on a master’s degree in this program and on a degree in computer science, business administration, mathematics, computer and systems engineering, or another related area.

Most students participating in this curriculum hold a bachelor’s or master’s degree in engineering, mathematics, the physical sciences, business administration, or management. Students with training in other disciplines such as economics and the social sciences are also encouraged to apply if their quantitative backgrounds include the equivalent of at least three semesters of calculus and linear algebra.

All applicants are required to take the Graduate Record Examination (GRE) except under extenuating circumstances.

For the master’s programs in operations research and statistics, no prerequisites are required. However, the following courses or their equivalents are required.

DSES-4640 Operations Research I (4 credit hours)
and
DSES-4750 Probability Theory & Applications. (4 credit hours)
and
DSES-4760 Mathematical Statistics (4 credit hours)
and
DSES-6050 Stochasitic Processes (3 credit hours)
or
DSES-6150 Advanced Probability for Statistical Inference (3 credit hours)
or
DSES-6820 Queuing Systems & Applications (3 credit hours)
and
DSES-6620 Simulation Modeling & Analysis (3 credit hours)
and
DSES-6060 Applied Multivariate Analysis (3 credit hours)
or
DSES-6140 Exploratory Data Analyses (3 credit hours)
and
Modeling Optimization Elective (3 credit hours)*

*Any course listed under the Modeling/Optimization Concentration except DSES-4770.

While the remainder of the program may be tailored to the student’s interest, the plan of study must include a concentration area. The concentration is a set of three or more courses (or nine credit hours) that reflects a logical progression for developing a base of expertise in an area of study. Concentrations will usually, but not always, include at least one of the core courses listed above. Listed below are several concentration areas and the courses that are applicable to them.

Doctoral Programs

Students working toward the Ph.D. in DSES may choose to major in industrial engineering, information systems, manufacturing systems engineering, operations research, or statistics. Advanced research and a dissertation in the chosen field are required in the doctoral program. In addition, Ph.D. students must complete the following:

• Institution requirements established by the Office of Graduate Education.
• Seminar in DSES research: Doctoral students must complete DSES-6900 during the fall semester of the first academic year of residency. This course is intended to introduce the student to the research environment at Rensselaer and to provide background on the process of doctoral research in DSES. Another intent of the course is to develop the student’s communication skills.
• Doctoral qualifying examination: Each student must take an oral examination covering the core areas of DSES. The basis for this examination includes DSES-4530, DSES-4750, DSES-4760, DSES-4770, and DSES-6500 or their equivalents. This examination is normally taken at the end of the second semester of residency. A more detailed description of this and other required examinations is available from the DSES doctoral program director.
• Doctoral area and advanced seminar requirement: Doctoral students must register for and complete DSES-6910 during the fall semester of the second academic year of residency. This seminar reviews methods for undertaking DSES research and requires students to prepare a research paper under the guidance of a faculty adviser. This requirement also includes course work in a selected major area. See the DSES doctoral program director for detailed documentation of DSES doctoral area requirements.
• Doctoral candidacy examination: Each student must take an oral candidacy examination after passing the DSES area requirement but before completing 75 credit hours of graduate work. This examination tests the candidate’s background for the proposed research, appropriateness of the thesis research, and the ability of the candidate to successfully complete the research. The thesis research proposal must contain at least one result that meets journal publishing standards.
• Doctoral dissertation and defense: Each student must write a doctoral thesis and give a formal oral public defense.

Apart from the seminar and advanced seminar in DSES research, doctoral area course requirements, and equivalent course material for the doctoral qualifying examination, there are no formal course requirements for the doctoral degree. However, the student is expected to develop in-depth knowledge in his/her dissertation area through appropriate course work, as well as supervised research. A plan of study is required, which must be approved by the thesis adviser and the DSES doctoral program director. Representative programs of study are available from the DSES doctoral program director.

Courses   Courses directly related to all Decision Sciences and Engineering Systems curricula are described in the Course Description section of this catalog under the department code DSES.