Systems Engineering

Systems Engineering

System Engineering

Established in 1961 as the nation’s first department of systems engineering, the Department of Systems and Industrial Engineering (SIE) has been consistently recognized internationally for its innovative contributions to large-scale system design involving human, technological, and informational assets. Graduates of our programs have gone on to create complex, interconnected systems for leading organizations around the world, driving exciting, reliable new developments.

Apart from being accomplished educators and researchers, our faculty have been involved in numerous projects requiring the use of large-scale systems across a wide variety of industries, including space and aeronautics, defense, transportation, manufacturing, energy, water, infrastructure, and homeland security. You’ll have the opportunity to connect directly with these groundbreaking leaders in the field, benefiting from their practical, real-world expertise.

With incredible technological advances being made in the field of software and systems development each and every day, there’s never been a more exciting time to pursue the real-world skills necessary to lead in this industry. Each program examines more than just technical skills, offering in-depth examinations of leadership and business fundamentals that can help you confidently pursue new professional opportunities.

To learn more about our online programs in systems engineering, click the links below:

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Course Descriptions

Quality, improvement and control methods with applications in design, development, manufacturing, delivery and service. Topics include modern quality management philosophies, engineering/statistical methods (including process control, control charts, process capability studies, loss functions, experimentation for improvement) and TQM topics (customer driven quality, teaming, Malcolm Baldridge and ISO 9000). Graduate-level requirements include additional readings and assignments/projects.
It is concerned with determining the probability that a component or system, whether simple or complex, will function as intended. The scope of this course includes: (1) Root cause analysis of critical failures, (2) reliability models of components and systems, (3) development of statistical methods for estimating the reliability of a product, (4) use of software tools to perform model development and analysis, and (5) methodologies to influence system designs. Graduate-level requirements include a term project that focuses on real-world implementations of the course material and/or original theoretical developments in the form of a technical paper. Project topics (e.g., system reliability optimization, physics-based reliability models, warranty data analysis) must be approved by the instructor.
Topics covered in this course include patents, trade secrets, trademarks, copyrights, product liability contracts, business entities, employment relations and other legal matters important to engineers and scientists. Graduate-level requirements include an in-depth research paper on a current topic.
Principles of the engineering sales process in technology-oriented enterprises; selling strategy, needs analysis, proposals, technical communications, electronic media, time management and ethics; practical application of concepts through study of real-world examples. Graduate-level requirements include a term paper on a course topic selected from a short list of topics, other graded components of the course and creation of a PowerPoint presentation to the class.
Application of principles of probability and statistics to the design and control of engineering systems in a random or uncertain environment. Emphasis is placed on Bayesian decision analysis. Graduate-level requirements include a semester research project.
Statistical methodology of estimation, testing hypotheses, goodness-of-fit, nonparametric methods and decision theory as it relates to engineering practice. Significant emphasis on the underlying statistical modeling and assumptions. Graduate-level requirements include additionally more difficult homework assignments.
Discrete event simulation, model development, statistical design and analysis of simulation experiments, variance reduction, random variate generation, Monte Carlo simulation. Graduate-level requirements include a library research report.
Survey of methods including network flows, integer programming, nonlinear programming, and dynamic programming. Model development and solution algorithms are covered. Graduate-level requirements include additional assigned readings and a project paper.
Unconstrained and constrained optimization problems from a numerical standpoint. Topics include variable metric methods, optimality conditions, quadratic programming, penalty and barrier function methods, interior point methods, successive quadratic programming methods.
An intensive study of continuous and discrete linear systems from the state-space viewpoint, including criteria for observability, controllability, and minimal realizations; and optionally, aspects of optimal control, state feedback, and observer theory.
The course will cover various modeling and simulation approaches used in studying traffic dynamics and control in a transportation network. The model-based simulation tools discussed include dynamic macroscopic and microscopic traffic flow simulation and assignment models. Models will be analyzed for their performance in handling traffic dynamics, route choice behavior, and network representation.
Quantitative models in the planning, analysis and control of production systems. Topics include aggregate planning, multi-level production systems, inventory control, capacitated and uncapacitated lot-sizing, Just-in-time systems and scheduling.
Focuses on principles of cost estimation and measurement systems with specific emphasis on parametric models. Approaches from the fields of hardware, software and systems engineering are applied to a variety of contexts (risk assessment, judgment & decision making, performance measurement, process improvement, adoption of new tools in organizations, etc.). Material is divided into five major sections: cost estimation fundamentals, parametric model development and calibration, advanced engineering economic principles, measurement systems, and policy issues. The graduate-level requirements include a final paper.
Fundamentals of Supply Chain Management including inventory/logistics planning and management, warehouse operations, procurement, sourcing, contracts and collaboration. Graduate-level requirements include an additional semester research paper.
Financial modeling and simulation of new technology ventures. Topics include Pro Forma financial statements construction, time value of money, accounting, valuation, and technology ownership issues. Entrepreneurship issues related to forming a company will be discussed. This course is intended for graduate students in science or engineering with little or no prior background in engineering economics.
Advanced techniques for statistical quality assurance, including multivariate statistical inference, multiple regression, multivariate control charting, principal components analysis, factor analysis, multivariate statistical analysis for process fault diagnosis, and select papers from the recent literature.
This is a three-credit course for well-qualified graduate students who have taken graduate-level statistics courses. The course provides a comprehensive introduction to the statistical principles and methods for reliability data analysis. This course will cover parametric, nonparametric, and semiparametric methods for modeling degradation data and failure time data with different types of censoring.
Emphasis on current research problems including simulation based control, distributed federation of simulations, and multi-paradigm (system dynamics, discrete event based, agent-based) simulations.

Course Requisites: SIE 431 or MIS 521.

Decomposition-coordination algorithms for large-scale mathematical programming. Methods include generalized Benders decomposition, resource and price directive methods, subgradient optimization, and descent methods of nondifferentiable optimization. Application of these methods to stochastic programming will be emphasized.

Course Requisites: SIE 544 or SIE 545.

Modeling and solving problems where the decisions form a discrete set. Topics include model development, branch and bound methods, cutting plane methods, relaxations, computational complexity, and solving well-structured problems.

Course Requisites: SIE 544.

This course is devoted to structure and properties of practical algorithms for unconstrained and constrained nonlinear optimization.

Course Requisites: SIE 544 or SIE 545.

Convexity, optimality conditions, duality, and topics related to the instructor’s research interests; e.g., stochastic programming, nonsmooth optimization, interior point methods.

Course Requisites: SIE 544 or SIE 545.

Modeling and design of complex systems using the Unified Modeling Language (UML), the Systems Modeling Language (SysML) and Wymorian System Theory. Applications come from systems, hardware and algorithm design. Course will emphasize architecture, requirements, testing, risk analysis and use of various systems design tools.

Course Requisites: SIE 554A.

Special topics in the analysis and design of transportation systems, including advanced traffic management, network routing, dynamic traffic estimation and assignment, network design, intermodal distribution and transportation, and intelligent transportation systems.

Course Requisites: SIE 305, SIE 321; SIE 540 or SIE 544; some knowledge of network modeling.

Individual study or special project or formal report thereof submitted in lieu of thesis for certain master’s degrees.
Research for the master’s thesis (whether library research, laboratory or field observation or research, artistic creation, or thesis writing). Maximum total credit permitted varies with the major department.