FAMU-FSU College of Engineering :: Department of Industrial Engineering

The graduate curriculum in quality engineering is designed to provide students with the tools and skills necessary to succeed as leaders in quality improvement. The more introductory courses are tailored to providing the latest methods best suited for direct application in any environment. The advanced topic courses are also applied, but stress the current peer-reviewed quality engineering research contributions to educate and motivate students for continued research. The number of graduate course offerings within the department is expected to increase substantially over the next two years.

EIN 5524 – System Modeling and Simulation (3). Prereq: ESI 3443, FORTRAN. Discrete event, continuous, and process simulation. Combined discrete/continuous simulation. Manufacturing systems modeling. Event graphs. Simulation languages and systems. Experimentation with models. Introduction to simulation-specific statistical problems. Model validation and verification issues. Design exercises.

ESI 5154 – Statistical Process Control (3). Prereq: ESI 4234. Advanced methods of statistical process control for univariate and multivariate processes. Methods for change point detection and estimation. Control chart performance comparisons. Process capability studies.

ESI 5247 – Engineering Experiments (3). Prereq: EIN 5417, EGN 3443. Introduction to designing experiments and analyzing their results. Intended for engineers and scientists who perform experiments or serve as advisors to experimentation in industrial settings. Students must understand basic statistical concepts. A statistical approach to designing and analyzing experiments is provided as a means to efficiently study and comprehend the underlying process being evaluated. Insight gained leads to improved product performance and quality.

ESI 5412 (FAMU) / ESI 5408 (FSU) – Applied Optimization (3). Prereq: ESI 3312. Optimization topics relevant to industrial operations and systems. Emphasis on basic modeling assumptions and procedure implementation. Topics shall include linear programming, nonlinear programming, discrete optimization, and large-scale optimization software. Design exercises. Please note: students enrolled through FAMU should register for ESI 5412, while students enrolled in FSU should register for ESI 5408.

ESI 5417 – Engineering Data Analysis (3). Prereq: EIN 3443 or equivalent. Analysis of experimental and observational data from engineering systems. Focus on empirical model building using observational data for characterization, estimation, inference and prediction.

ESI 5451 – Project Analysis and Design (3). Prereq: EGN 3613, ESI 3312. Project analysis and evaluation, utilizing networks and graph theory, advanced engineering economy, simulation procedures and other evaluation software. Project implementation topics, including resource shortfalls and expediting. Case studies and design exercises.

EIN 5930 - Data Mining (3). Prereq: EIN 5247. Data mining is a growing field that combines statistics, information science and computer algorithms to discover knowledge and patterns from massive data sets with a large number of variables and observations. This course introduces the core concepts and ideas in data mining including classification, clustering, feature selection, data reduction, decision trees, neural networks, support vector machines, model validation and selection, data preprocessing and missing data handling. Applications covered in the course include consumer credit scoring, fraud detection, sensor data monitoring, among many others.

EIN 5930A - Response Surfaces and Process Optimization (3). Prereq: EIN 5247. Response surface methodology effectively combines statistically based experiment designs, empirical model building, and optimization methods to achieve this objective. Other course topics include restrictions on randomization, mixture experiments (ingredient-type factors with responses that depend on the relative mix of ingredient components) and robust design (including known nuisance sources of variability directly into the design and analysis).

EIN 5930B - Advanced Engineering Data Analysis (3). Prereq: ESI 5417. This course is designed to enhance an engineer’s body of knowledge regarding empirical modeling building, especially for engineering data that does not conform to classical assumptions. This course assumes that students are grounded in fundamental statistical principles and have taken a graduate level empirical modeling course. The topics go beyond ordinary least squares methods for multiple linear regression including robust regression, nonparametric methods, nonlinear regression, classification methods, flexible regression methods, ridge regression, and generalized linear models. This course will serve to broaden and enhance model building capabilities to better prepare them for engineering analysis, and to provide a forum further investigating research topics, eventually leading to publication.