Department Head: Charles J. Malmborg (Acting)
Director, Doctoral Program: Thomas Willemain
Director, Undergraduate Program: Charles J. Malmborg
Department Home Page: http://www.dses.rpi.edu
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 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, bioinformatics, and systems engineering.
The Department of Decision Sciences and Engineering Systems offers programs in industrial and management engineering, service and manufacturing systems engineering, and operations research and 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 new department. The program in Manufacturing Systems Engineering was inaugurated in the fall of 1992, and has been updated to the current degree program in Service and Manufacturing Systems Engineering. This program is designed to emphasize modeling, statistical, computer, and management skills as they relate to service operations and 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
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, facility design, information integration for design and manufacturing, control systems, and methodologies to integrate statistical quality control with computer graphics.
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- intensive, real-time decision support approaches.
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 data- bases 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.
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 real-time data fusion and analysis, 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 two 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:
Center for Services Research and Education (CSRE)
The goal of the CSRE is to enhance our understanding of the service 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 service systems and can be employed to enhance productivity and competitiveness. The CSRE takes a holistic approach to the multifaceted service sector and brings together experts from engineering, marketing, psychology, economics, and management policy and organization, among others. Experts examine the comon elements that characterize all aspects of the service sector and develop generic principles that apply across the wide spectrum of services, including the advancement of a focus in service systems engineering.
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.
Berg, D. —NAE, Ph.D. (Yale University)Institute Professor of Science and Technology (joint in Lally School of Management and Information Technology); 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.
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.
Holguin-Veras, J.—(Civil and Environmental) Ph.D. (University of Texas/Austin); transportation modeling and transportation economics, information technology, information systems, optimization techniques.
Hughes, G.— (Economics) Ph.D. (Princeton University); global economics, economics of information technology; Clinical Professor.
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.
Mitchell, J.E.— (Mathematical Sciences) Ph.D. (Cornell University); mathematical programming integer programming, interior point methods, column generation methods, financial optimization, stochastic programming.
Pang, J.S.—(Mathematical Sciences) Ph.D. (Stanford University); Mathematics of Finance and Mathematical Statistics.
Tien, J.M.—NAE, Ph.D. (Massachusetts Institute of Technology) Yamada Corporation Professor (joint in Electrical, Computer, and Systems Engineering; IT); systems modeling, queuing theory, public policy and decision analysis, computer performance evaluation, and information and decision support systems, expert systems, computational cybernetics.
Wallace, W.A.—Ph.D. (Rensselaer Polytechnic Institute)(joint in Civil Engineering; Cognitive Sciences; IT); decision support systems, environmental management modeling process, disaster management.
Willemain, T.R.—Ph.D. (Massachusetts Institute of Technology); probabilistic modeling, data analysis, forecasting.
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; Clinical Associate Professor.
Aboul-Seoud, M.—Ph.D. (University of Louisville); reliability engineering, quality control, operations research; Clinical Assistant Professor.
Chan, W.K.—Ph.D. (University of California at Berkeley); discrete event simulation, design and analysis of manufacturing and service systems, mathematical statistics, queuing theory.
Fukunari, M.—Ph.D. (Rensselaer Polytechnic Institute); material flow logistics, simulation modeling and analysis, optimization, stochastic processes, queuing theory, design of experiments.
Krishnamurthy, A.—Ph.D. (University of Wisconsin-Madison); queuing models and simulation based approaches for design and analysis of manufacturing systems, quick response manufacturing, queuing networks, analytical models in fabrication/assembly systems.
Osborn, B.E.—Ph.D. (Brown University); regression analysis, optimization, statistics.
Bennett, K. — (Mathematical Sciences) Ph.D. (University of Wisconsin); mathematical programming, operations research, artificial intelligence.
Bringsjord S.— (Cognitive Sciences) Ph.D. (Brown University); logic and artificial intelligence, foundations of artificial intelligence and cognitive science, computation creativity.
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.
Kelly, L.J.— (Rensselaer at Hartford) Ph.D. (University of Connecticut); statistics, operations management.
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.
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
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.
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.
Ravichandran, T.—(Lally School of Management and Technology) Ph.D. (Southern Illinois University, Carbondale); management information systems.
Yang, Y.—(Cognitive Science) Ph.D. (New York University); cognitive psychology, thinking, reasoning, decision-making, cognitive science.
Zaki, M.J.—(Computer Sciences) Ph.D. (University of Rochester); design of efficient, scalable, and parallel algorithms for various data mining techniques.
Affiliated Assistant Professor
Martin, L.L. —(Chemical and Biological Engineering) Ph.D. (University of California at Los Angeles); process systems engineering, design for waste minimization and pollution prevention.
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.
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.
Raghavachari, M. —Ph.D. (University of California at Berkeley); statistical inference, quality control, multivariate methods, scheduling problems.
Sullo, P. —Ph.D. (Florida State University); reliability, life testing, statistical quality control, management, biostatistics, industrial statistics.
Wilkinson, J.—Ph.D. (University of North Carolina); regression modeling, statistical analysis.
* 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 2007 Board of Trustees meeting.
Objectives of the Undergraduate Curriculum
While certain objectives of an undergraduate education in engineering are common to all programs, there are subtle but important differences ensuring that all graduates have specialized technical knowledge in their chosen field. In this regard, graduates of the Department of Decision Sciences and Engineering Systems baccalaureate program in Industrial and Management Engineering will:
- Have a solid foundation in all of the fundamental areas of industrial and management engineering emphasizing a total integrated systems perspective and reflecting the unique strengths of Rensselaer’s program including in-depth knowledge of manufacturing and service systems, effectiveness in the management of people and systems, and the creative application of computing and other technologies.
- Be creative and innovative designers of systems, processes, facilities, services, products and equipment with strong analytical skills and a sufficient practical understanding of real world problems to be skillful at identifying, modeling, analyzing and solving challenging problems.
- Be effective oral communicators, good technical writers, and have a solid foundation for using communication media of all types to facilitate their strengths as contributors and leaders of diverse teams.
- Be broadly educated in the humanities, social sciences, and engineering professionalism which will inform their socially responsible and ethical practice of industrial and management engineering. They will understand the importance of life-long learning and be capable of and motivated to pursue continued growth and learning throughout their careers.
- Have a solid foundation in math and science which they can effectively apply in the practice of industrial and management engineering.
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.
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 in the programs.
Students working toward the Ph.D. in DSES may choose to major in industrial engineering, information systems, service and 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:
- Institute 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: The doctoral qualifying exam consists of two components — a written exam component and a research component. The written exam covers the core areas of DSES and the basis for this examination, including DSES 4530, DSES 4750, DSES 4760, DSES 4770, and DSES 6500 or their equivalents. A more detailed description of this and other required examinations is available from the DSES doctoral program director.
- Doctoral area requirement: Doctoral students must register for and complete 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 45 credit hours of graduate work beyond the master’s degree. 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 in DSES research, doctoral area course requirements, and equivalent course material for the doctoral qualifying examination, there are no additional 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 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.