Rensselaer Catalog 2012-2013 [Archived Catalog]
Industrial and Systems Engineering
Department Head: Charles J. Malmborg
Director, Doctoral Program: David Mendonca
Director, Master’s Program: William J. Foley
Director, Undergraduate Program: Charles J. Malmborg
Department Home Page: http://www.ise.rpi.edu
The Department of Industrial and Systems Engineering 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. The common theme throughout the department’s academic programs is the use of mathematical, statistical, and computational/simulation models to better understand, predict and optimize complex engineering, managerial, operational, and physical systems.
Research Innovations and Initiatives
The department’s research is focused on core disciplinary strengths in Industrial and Systems Engineering (ISE). ISE involves the application of mathematical, computational, statistical and information science methods to model, analyze and solve complex decision problems in engineering, business, and social systems. ISE employs methods of mathematical programming, queuing theory, computational optimization, decision analysis, applied statistics, database systems, soft computing, 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. It is distinctive from management and economics in the use of an engineering approach to design and analyze enterprise processes to optimize performance. It is distinctive from computer science in its focus on the design of data and knowledge systems as the organizational nerve center where operations and enterprise systems are integrated.
The department’s faculty research aligns directly with these core strengths to exploit dynamically evolving opportunities of high relevance in such areas as Adaptive Supply Chains, Social and Cognitive Networks, Homeland Security, Service Systems Engineering, Energy and Environmental Systems, and Biotechnology.
Cognitive and Social Networks
The department’s research thrust in cognitive and social networks relates to the development of computational technologies focused on the application of artificial intelligence, soft computing, data fusion, information systems, and data mining. Key applications include threat detection in social network communications, issues of trust and ethics in online communities, visualization in media and design, emergent and improvisational organizational responses to natural and unnatural disasters, and group and individual behavior in dynamic social systems. This research lies at the intersection of operations research, systems engineering, and psychology. The unifying thread is the use of cyber-infrastructure to enhance the information value chain from data, to information, to knowledge to decision making. The research has yielded practical techniques and algorithms for such tasks as automated mining of media files and social network communications, modeling organizational responses to unplanned events, and the impact of interdependencies among infrastructural sub-systems in urban areas.
An excellent example of the department’s 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 could not function without both the logistical infrastructure to collect, store, and move goods and the information infrastructure to monitor and control the flow of those goods over the network.
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. The 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 the methodologies of Industrial and Systems Engineering can address major challenges in both the ability of supply chains to adapt to evolutionary change and 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 facing supply chains in the 21st century where criteria related to resiliency and sustainability will rival cost as a dominant driver in decision making. The department’s research in adaptive supply chains is expanding the theoretical frameworks for understanding, modeling, and simulating interdependent supply chains under short-term disruptive conditions as well as their adaptability over the system life cycle.
Other Important Research Themes
ISE research in Energy and the Environment models self-reconfigurable power grids with cyber-infrastructure and distributed sensors using agent-based methodologies. Related research in this application area involves load forecasting, advanced simulation models to assess the impact of climate change, and proton exchange membrane fuel cell manufacturing. ISE research in Service Systems Engineering builds on the complementarity of services and manufacturing in applying cyber-infrastructure to produce and provide on-demand, mass-customized services. The key characteristics of these services include scalability, asynchronous co-production, and human-centered assistance through cyber-infrastructure. ISE research in Biotechnology uses computational intelligence for computer-aided drug design, simulation tools for modeling the spread of infectious diseases, and the development of text-mining techniques in bioinformatics.
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.
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.
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.
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; neural networks, fractals, chaos, and wavelets for time-series analysis; data mining and computational intelligence.
Mendonca, D.—Ph.D. (Rensselaer Polytechnic Institute) ; decision analysis, decision making in emergent environments, group decision making, human performance, infrastructure systems.
Ryan, J.—Ph.D. (Northwestern University); Bayesian methods for decision support systems, stochastic optimization methods for logistical systems, stochastic models for inventory control and supply chain management, analysis of make-to-stock production/inventory systems, service parts logistics, decision models for large-scale condition monitoring.
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.
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.
Aboul-Seoud, M.—Ph.D. (University of Louisville); reliability engineering, quality control, operations research; Clinical Assistant Professor.
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.
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.
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, quality management, biostatistics, industrial statistics.
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.
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 2012 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: 1.) thorough understanding of manufacturing systems, service systems and supply chains, 2.) knowledge of engineering relationships to the planning, organization, implementation and control of human centered systems, and 3.) 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 Industrial and Management Engineering Program at Rensselaer is accredited bythe Engineering Accreditation Commission of the Accreditation Board for Engineering and Technology (ABET), 111 Market Place, Suite 1050, Baltimore, MD 21202-4012 - telephone: (410) 347-7700, http://www.abet.org.
The ISE 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, computational 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 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 manufacturing, services, 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.
This curriculum requires a minimum of 128 credit hours and completion of the course requirements shown in the typical four-year program presented in the Programs section of this catalog.
Industrial and Management Engineering Senior Portfolio
A recommended practice for engineers and others who use creativity and inventiveness to solve problems or create a design or artifact is maintaining a portfolio of their work. Students enrolled in Rensselaer’s bachelor of science program in Industrial and Management Engineering are required to submit a professional portfolio prior to graduation. The portfolio is a collection of the student’s work representing examples of the professional skills and corroborating achievement of specific educational outcomes gained through the undergraduate program. Upon declaring Industrial and Management Engineering as the undergraduate major, students should contact the Undergraduate Program Director to obtain materials and instructions for assembling the senior portfolio.
Special Undergraduate Opportunities
Cooperative Education Program
The department 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 Center for Career and Professional Development.
The Industrial and Systems Engineering 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. All applicants to the IME master’s programs must take the Graduate Record Exam (GRE).
The Industrial and Systems Engineering Department offers the Ph.D. in Decision Sciences and Engineering Systems. 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 from the restricted list of approved doctoral courses which are subject to adviser and Doctoral Program Director approval. The approved list of courses can be found under the Ph.D. in Decision Sciences and Engineering program information.
Courses directly related to all Industrial and Systems Engineering curricula are described in the Course Description section of this catalog under the department code ISYE.