Graduate Courses and Syllabi
The following is a list of graduate courses taught by the Department of Industrial and Systems Engineering. The catalog description is given while the title of the course links to a current syllabus.
EIN 6176 : Advanced Quality Management and Engineering for Business Processes
Credits: 3; Prereq: introductory statistics or consent of instructor.
This course will explore both the philosophy of continuous improvement and methodology for applying team problem solving to the manufacturing and service industries. The course will include hands-on application of basic statistical quality tools; introduction to quality function deployment; concurrent engineering; business process reengineering; process analysis; benchmarking, and a team project.
EIN 6357: Advanced Engineering Economy
Credits: 3; Prereq: STA 4321.
This course will explore economic analysis of capital expenditure decisions, financial mathematics, microeconomics, game theory, utility theory, and decision-making under risk and uncertainty.
EIN 6422: Manufacturing Management
Credits: 3; Prereq: ESI 6314 and undergraduate probability and statistics.
This course will cover the importance of management decisions in relation to total quality management, just-in time manufacturing, concurrent engineering, material requirements planning, production scheduling, and inventory control.
EIN 6905: Web-Based Decision Support Systems for Industrial and Systems Engineers
This course is an introduction to the Internet and e-commerce; Internet tools and technologies necessary for the development of Web-based decision support systems; Designing and implementing Web-based decision support systems arising in the practice of industrial and systems engineering using popular software packages.
EIN 6905: Data Analysis and Data Mining in Systems Engineering
Credits: 3; Prereq: ESI6314 or (ESI4327C, STA4322 and ESI4312)
This course will give insight into the theory background and applications of supervised and unsupervised learning algorithms. Selected topics include Decision Trees, Bayesian Networks, Support Vector Machines, K-Means clustering, Biclustering and Principle Component Analysis. In addition, we will cover material on recent emerging topics such as Robust Data Mining and Massive Data Sets.
ESI 6314: Deterministic Methods in Operations Research
Credits: 4; Prereq: calculus through differential equations, knowledge of linear algebra, and experience using mainframes or PCs.
This course is an introduction to basic models and their solutions with modern computer packages. Emphasis will be placed on modeling, computer solution, sensitivity analysis with minimal reference to model theory and development of algorithmic methods. OEMP students here.
ESI 6325 Applied Probability Methods in Engineering (APME)
Credits: 3; Prereq: calculus, differential equations, undergraduate probability, and statistics.
This course will include applied probability theory and statistics, reliability engineering, quality control, robust design, forecasting, Markov processes, and queuing theory.
ESI 6323: Models for Supply Chain Management
Credits: 3; Prereq: prior course work in linear programming, probability, and stochastic processes.
Essential elements include controlling and coordinating activities such as order processing, purchasing, material storage and handling, production scheduling, packaging, transportation, and setting customer service standards.
ESI 6417: Linear Programming and Network Optimization
Credits: 3; Prereq: matrix theory.
This course will include formulation and solution techniques for network flow and linear programming problems, algorithms for network optimization, duality theory, sensitivity analysis, and the simplex method theory and computation.
ESI 6420: Fund Math Programming
Credits: 3; Prereq: Basic understanding of linear algebra, calculus, ability to write simple codes with MatLab or C
Introduction to Mathematical Programming, with an emphasis on fundamental mathematical concepts used in optimization, classical optimization theory and applications of optimization in engineering. Focus on convex analysis (convex sets, separation theorems, convex functions), optimality conditions (Fritz-John & Karush-Kuhn-Tucker) and lagrangian duality.
EIN 6510 Principles of Manufacturing Systems Engineering
Credits: 3; Prereq: calculus through differential equations.
This course is an introduction to modern manufacturing systems including reference to components of product and process design, computer-integrated manufacturing and automation, and current areas of development and research.
ESI 6492: Global Optimization
Credits: 3; Prereq: linear and nonlinear programming.
This course will include properties of nonconvex functions, convex envelopes, duality, complexity issues, applications of global optimization, software issues, algorithms for quadratic programming, concave minimization, Lipschitz optimization, and nonconvex network flow problems.
ESI 6529: Digital Simulation Techniques
Credits: 3; Prereq: computer programming and probability theory.
This course will include the computer programming aspects of digital simulation, deterministic simulation, stochastic simulation, and the use of simulation languages. Project Description
ESI 6546: Stochastic Modeling and Analysis
This course will explore Stochastic processes, with emphasis on model building and probabilistic reasoning. Additionally, the course will review elementary probability theory, poisson process, renewal theory, Brownian motions, discrete and continuous time Markov chains, random walks, martingales, applications in queuing, reliability, inventory theory, logistics, and finance.
ESI 6555: Systems Management
Credits: 3; Prereq: Linear algebra, calculus, probability
Systems engineering is a discipline that addresses the management of systems of increasing complexity in military, industrial, commercial, and civil areas. This course will acquaint students with concepts of systems and the role systems engineering plays in their development. It will also provide a basic framework for planning and assessing system development and how systems analysis methods and techniques are integrated within the systems engineering process
ESI 6912: Fundamentals of Mathematical Programming
This course is an introduction to Mathematical Programming with an emphasis on fundamental mathematical concepts used in optimization, classical optimization theory and applications of optimization in engineering. Focus will be placed on convex analysis (convex sets, separation theorems, convex functions), optimality conditions (Fritz-John & Karush-Kuhn-Tucker) and lagrangian duality.
ESI 6552: Systems Architecture
Systems engineering is a discipline that addresses the design and management of systems of increasing complexity in military, industrial, and commercial applications. This course will acquaint students with concepts of systems architecture and the application of those concepts to real-world systems.
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ESI6553: System Design, 3 credits
Prerequisite: calculus, linear algebra, basics of statistics, ESI 6314.
Broad introduction to systems engineering and the structured approaches needed to design complex systems. Emphasizes formulation of systems problems and approaches to their solution. Introduces basic mathematical techniques for dealing with systems design.