Department Head: Charles J. Malmborg
Director, Doctoral Program: Mark J. Embrechts
Director, Undergraduate Program: Charles J. Malmborg
Department Home Page: http://www.dses.rpi.edu
The formation of this 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 a better understanding of how information technology and quantitative analysis and modeling can support individuals, groups, and systems in problem solving and decision making. These objectives are achieved by extending and integrating knowledge from the disciplines of Information Engineering, Operations Engineering, and Enterprise Engineering.
The department offers degree programs at the bachelor’s, master’s and doctoral levels including the bachelor’s and master’s degree in Industrial and Management Engineering and the doctoral degree in Decision Sciences and Engineering Systems. Curricula in management engineering and/or industrial engineering have been offered at Rensselaer since 1933. An interdisciplinary graduate program in operations research and statistics (OR&S) was established at Rensselaer in 1967 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. Today, 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
The department’s research is focused on the core disciplines of Information Engineering, Operations Engineering, and Enterprise Engineering.These areas can be concisely defined as follows:
Information Engineering – the application of information science, computer science, and mathematics in system design and analysis as it relates to the creation, fusion, processing, management, and deployment of data, information, and knowledge.
Operations Engineering – the application of mathematical, scientific, and computational methods to decision problems in engineering design and the modeling and analysis of technical, business, social, and physical systems.
Enterprise Engineering – the application and development of management science and engineering principles to the design and control of enterprises.
Information Engineering employs results from data and knowledge engineering, computational statistics, and information systems to design and provide information infrastructure to support enterprise operations. In contrast to computer science and other related disciplines, information engineering focuses on the design of data and knowledge systems as the organizational nerve center where operations and enterprise systems are integrated. The methodological foundations of Information Engineering are rooted in soft computing, database systems, and forecasting. Emerging areas of research include the fusion, analysis, and management of real time data streams from large scale distributed sources; the design and administration of cyber-infrastructure for digital enterprises; and open connection technology such as Web services and service-oriented architecture. Popular application areas encompass the science of collaboration, intelligent transportation, and computerized manufacturing and services.
Operations Engineering employs methods of mathematical programming, queuing theory, computational optimization, agent-based modeling, engineering statistics, and discrete event simulation for solving problems related to the design, planning, and operation of complex systems where intelligent coordination is necessary to achieve optimal performance.
Enterprise Engineering employs results from information systems, management, organization theory, and microeconomics to design, rationalize, and control large-scale enterprise systems. Operations and Enterprise Engineering are distinctive from management and economics in the use of an engineering approach to design and plan enterprise processes to optimize performance. Enterprise Engineering is distinguished from Operations Engineering based on its methodological foundations in strategy and policy, entrepreneurship, organization design, production functions, and social networks.
The department’s faculty research aligns directly with these three core strengths to exploit dynamically evolving opportunities of high relevance in such areas as Homeland Security, Intelligent Transportation Systems, Energy and the Environment, and Biotechnology. However, the department has two primary research themes directly linked to our three core strengths;Services Engineering and Adaptive Supply Chains.
Adaptive Supply Chain
The department’s research in adaptive supply chains deals with the logistics of efficiently deploying finite resources to assemble, transport, sustain, and distribute people and goods, thereby facilitating the fulfillment of demand associated with economic commerce, national defense, disaster response, and/or humanitarian aid. Our focus is on efficient and integrated coupling of supply with distribution network resources from a total integrated systems perspective. The functional scope of Adaptive Supply Chains spans production/procurement, materials management, storage, transport, routing, warehousing, dispatching, delivery, and service. Its contextual scope spans production, transportation, military, health, maritime, and communications systems. All of these systems are characterized by complex interdependencies where methodologies of Information, Operations, and Enterprise Engineering can address major challenges in both the ability of supply chains to adapt to evolutionary change and to respond to planned and unplanned disruptive events. The current body of design and modeling research in this area focuses on life-cycle cost minimization under steady state conditions, sequential supply and demand management, and predictable asset and material values. This traditional approach is clearly insufficient to deal with the challenges that will be posed for supply chains in the 21st century, where criteria related to resiliency and sustainability will challenge cost as a dominant driver in decision making. Research is needed to expand the theoretical frameworks for understanding, modeling, and simulating interdependent supply chains under short-term disruptive conditions as well as their adaptability over evolutionary life cycles.
An excellent example of our research in this area is in systems for disaster response and recovery. Recent events remind us of the global importance of natural, technological, and willful disasters. Such critical events precipitate a wide range of impacts on the interconnected, complex systems that constitute our infrastructure for food, transportation, power, housing, and medical supplies. These technological systems are more vulnerable because they are interdependent; disruptions in one can spread to others, causing cascading and potentially catastrophic failures. This vulnerability is exacerbated by advances in communications and computing technologies that are now integral to the operations of our infrastructure systems. For example, efficient and effective global supply chains such as those used by Wal-Mart and Dell could not function without both the civil infrastructure to collect, store, and move goods and the information to monitor and control the flow of those goods over the network. Therefore, disruptions to either the civil infrastructure or the information infrastructure could negatively impact our economy. Furthermore, both the civil infrastructure and the information infrastructure could be dependent upon other infrastructures such as electric power and the cyber-infrastructure, i.e., Internet.
Services Engineering requires and spawns significant engineering innovations at the levels of Information, Operations, and Enterprise. It builds on the complementarity of services and manufacturing. A number of our faculty members are recognized for seminal intellectual contributions that have played a major role in defining this field. Our current emphasis in this area aligns our effort with leading industrial programs in research and education and uses this foundation to engage government funding that supports university-industry collaboration. On the basis of the new scientific results and human resources built through this collaboration, we bring research in this area to the basic science level.
Our research examines the use of societal as well as enterprise cyber-infrastructure to produce and provide on-demand services to persons and enterprises that are connected by cyber-infrastructure. The key characteristics of these connected services include digital connection, service scalability, asynchronous co-production, and human-centered assistance through cyber-infrastructure. A signature technical foundation is extended cyber-infrastructure, which couples embedded data and metadata, knowledge, and analytics with computing and telecommunications infrastructure. The characteristics ofconnected services and the supporting cyber-infrastructure foundation can support massive concurrent virtual configurations and become the enabler and object of innovation to create new types of firms and production functions. Our research is aimed at helping to develop, rationalize, and optimize these characteristics to create new web enabled services for individuals and enterprises. In parallel, closely related research in global supply chains and the integration of manufacturing and services involves significant research challenges in optimization, queuing theory, data mining, statistical analysis, and simulation.
Other Important Research Themes
Other important themes in our department’s research focus on Homeland Security, Intelligent Transportation Systems, Energy and the Environment, and Biotechnology. Homeland Security relevant research relates to applications including (1) explosives detection, (2) text mining, (3) data fusion, and (4) intent dynamics in social networks. Data fusion represents a set of methodologies intended to build better diagnostic systems by combining different individual diagnostic techniques in such a way that the whole is more than the sum of the parts. One of the key drivers for data fusion is the need to boost the performance of different security-related detection techniques to increase overall specificity without an unreasonable number of false positives. Intent dynamics is another promising security-related application where the goal is to automatically identify in media files the occurrence of interesting and unusual events. An obvious application is the flagging of unusual events in sensor-based or camera-based surveillance systems.
In partnership with Rensselaer’s Center for Infrastructure and Transportation Studies, research in intelligent transportation systems investigates the use of information technology to integrate multiple transportation systems, e.g. highways, transit, railroads, maritime, and inland waterways, into a seamless system that delivers significantly improved results. This transformation and its focus on safe, secure, efficient operations with only a few, targeted, new construction projects presents an opportunity for reducing both energy consumption and environmental impact through the development of new paradigms of decision making that foster an appropriate consideration of the broader impacts of transportation, and efficient transportation operations supported by innovative computing and information technologies. Concurrent with this need to manage traffic operations is the ever-increasing availability of data and the techniques necessary to store, analyze, and present it to support real-time decision making systems.
Research in Energy and the Environment relates to such areas as proton-exchange-membrane fuel cell manufacturing and self-reconfigurable power grids with cyber-infrastructure and distributed sensors. Related areas of research in Information, Operations, and Enterprise Engineering in this application area relate to load forecasting and the use of advanced simulation models to assess pandemic events and the impact of global warming. Biotechnology is another important application area using computational intelligence for computer-aided drug design. This work has made a major contribution through the Rensselaer Center for Exploratory Cheminformatics and Chemometrics Research. Other biotechnology applications of our research emphasize the application of Operations Engineering in the development of new simulation tools for multi-phase fluid flow, electromagnetic field and quantum-mechanical calculations, and modeling the spread of infectious diseases. The use of machine learning techniques for micro-array assessment and text-mining techniques in bioinformatics represent additional areas where our core strengths contribute to biotechnology research.
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.
Holguin-Veras, J.—(Civil and Environmental) Ph.D. (University of Texas/Austin); transportation modeling and transportation economics, information technology, information systems, optimization techniques.
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.
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.
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.
Ryan, J. - Ph.D. (Northwestern University); Bayesian methods for decision support systems, stochastic optimization methods for logistical systems, stochastic models for inventory control and suppy chain management, analysis of make-to-stock production/inventory systems, service parts logistics, decision models for large-scale condition monitoring.
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); materials flow logistics, simulation modeling and analysis, optimization, stochastic processes, queuing theoy, design of experiments.
Sharkey, T.—Ph.D. (University of Florida); mathematical programming, network algorithms, combinatorial and computational optimization, supply chain logistics, demand allocation based supply chain optimization models, nonlinear network design problems.
Ukkusuri, S.— Ph.D. (Civil and Environmental); Ph.D. (University of Texas/Austin); transportation network modeling, traffic operations, stochastic optimization, transportation safety and security.
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.
Tien, J. – NAE, Ph.D. (Massachusetts Institute of Technology); systems modeling, queuing theory, public policy and decision analysis, computer performance evaluation, and information and decision support systems, expert systems, computational cybernetics.
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 2008 Board of Trustees meeting.
Objectives of the Undergraduate Curriculum
While certain objectives of an undergraduate education in engineering are common to all disciplines, there are subtle but important differences ensuring that all graduates have specialized technical knowledge in their chosen field. Three to five years after graduation, graduates of Rensselaer’s Bachelor’s program in Industrial and Management Engineering will:
- Exhibit a total integrated systems perspective enabling: a.) thorough understanding of manufacturing systems, service systems and supply chains, b.) knowledge of engineering relationships to the planning, organization, implementation and control of human centered systems, and c.) the effective application of information through computing and other emerging technologies.
- Be creative and innovative designers of systems, processes, facilities, services, products, organizational teams, and equipment with an understanding of the stochastic nature of management systems enabling the skillful identification, modeling, analysis, solution, and management of real world problems.
- Be effective oral and written communicators with a solid foundation for using communications media and interpersonal skills to facilitate their roles as contributors and leaders of diverse teams.
- Be broadly educated in the humanities, social sciences, and engineering professionalism which informs their socially responsible and ethical professional practice.
- Understand the importance of life-long learning and be capable and motivated to pursue continued growth, learning and innovation throughout the professional career.
- Apply a solid foundation in math and science in professional practice.
The department offers an undergraduate 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 services systems engineering, 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 department 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.
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 offers the Master of Science and Master of Engineering degrees in Industrial and Management Engineering. Both degrees require a minimum of 30 credit hours. The Master of Science degree requires a thesis. The Master of Engineering degree is a non-thesis option. In general, all applicants to the IME Master’s programs must take the Graduate Record Exam (GRE). Requirements for the Master’s programs include the following courses or their equivalents:
The department offers the Ph.D. in Decision Sciences and Engineering Systems (DSES). Students must select one major area and two minor areas from among three specializations including Information Engineering, Operations Engineering, and Enterprise Engineering. In general, all applicants to the Ph.D. program must take the Graduate Record Exam (GRE). During the first year of residency, doctoral students are required to elect courses in these areas from the restricted list shown below subject to adviser and Doctoral Program Director approval:
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.