Curriculum

Planning and operations models are used in a wide variety of applications. This course focuses on developing problem formulations that are appropriate for the situation at hand. The course will use a number of applications from industrial, mechanical, civil, and electrical engineering; financial optimization models; health care systems; environmental ecology; and forestry. The problems will span many types of solution methods, such as linear programming, integer programming, quadratic assignment problems, nonlinear convex problems, and black-box models. Multi-criteria optimization will be discussed, along with how to incorporate randomness into optimization models, such as chance-constraint programming and scenario-based stochastic programming.

This course is intended for first-year graduate students with the objective of teaching them how to account for sources of short- and long-term uncertainties in design, operation, and planning of engineering systems; engineering applications in energy, transportation, and production systems; and the use of software packages for problem solving will be emphasized. Two parts will be included: Part I deals with the basics of probability and stochastic processes and Part II deals with risk and decision making under uncertainty. Prior probability knowledge is required.

This course focuses on the application of data analytics tools to the design and improvement of engineering systems including semiconductor manufacturing, energy systems, transportation systems, and others. The course covers database access, descriptive analytics, signal processing, classification, predictive analytics, regression, and clustering analysis.

This course includes lectures by invited speakers, faculty, and graduate students on current research topics in industrial and systems engineering.

This course addresses the design, analysis, modeling, and optimization of selected energy systems (including conventional fossil fuels and renewable wind and solar). This course will provide the basis for applying mathematical modeling and optimization techniques in energy systems. A set of projects and case studies focused on modeling and optimization of a variety of energy systems will be assigned to students and discussed in detail. The course will have hands-on experience with data collection, experimentation, simulation, and optimization tools as they apply to energy systems.

This course focuses on the application of concepts and methods for understanding the dynamics of technological change. Major themes include issues in EM technology assessment, technology transfer, and strategic management of technology and technology focused organizations.

This course features the methods and techniques of operations research applied to the design and analysis of marketing and distribution systems. Topics include sales forecasting, single and multiechelon inventory and distribution systems, and routing and scheduling of product delivery.

The course covers computational methods in modeling, planning, and control of production systems; importance sampling, MCMC, numerical methods; artificial intelligence techniques; exact and heuristic search methods; and computational strategies for larger-scale systems.

The course includes investigations in selected areas of industrial and systems engineering and operations research.

The course focuses on understanding the state of technology in discrete, batch, and continuous manufacturing; hands-on experience.

This course includes discrete event simulation applied to problems in manufacturing, inventory control, and other engineering systems. Topics include simulation languages, estimating system operating characteristics, comparing alternative system designs, and validating approximate analytical models.

The course objective is to teach students to use the right combination of classical and AI based tools to solve real life decision and control problems. Case studies and examples in decision making and control are discussed. The course also includes a mini project.

Students in this course utilize applied data analytics and AU methodologies in energy projects. Activities include:

• Recall the fundamentals of statistics and probability theory
• Demonstrate how data analysis can be applied in an energy project.
• Develop data collection and data visualization from frameworks for energy data processing.
• Make statistical inference from energy data samples.
• Justify major machine learning algorithms to develop an end-to-end approach to analyze an energy problem.
• Identify and develop the best machine learning algorithm options for specific types of energy data.
• Hands on Python programming for energy analytics.

This course features analysis of production engineering, with emphasis on planning and control of manufacturing and service systems.

This course covers the design of automated and computer-integrated manufacturing systems using programmable automation. Topics include modeling of discrete and continuous control systems, design and analysis of control architecture, implementation of programmable controllers, and shop floor data acquisition systems.

This course provides an introduction to computational modeling and optimization of manufacturing processes. Topics include modeling and optimization of precision manufacturing processes (micro-machining), advanced manufacturing processes (laser and energy beam based), and additive manufacturing processes (selective laser sintering and melting). There is an emphasis on process physics and analytical and computational methods to predict and optimize process performance and product quality.

This course covers economic decision models for engineers involving allocation of resources; evaluation of strategic alternatives; advanced risk and uncertainty analysis; and weighing and evaluating nonmonetary factors.

The focus of this course is on quality management philosophies, Deming, Juran; quality planning, control, and improvement; quality systems, management organizations for quality assurance. The role of operations research is discussed.

This course provides an introduction to data mining; topics covered include concepts and applications of data mining, and an overview of data mining tools and methods such as classification, feature extraction, prediction, clustering, and anomaly detection.