Quality Control and Reliability Engineering

Prerequisites: IOE 366 or Stats 401 (C- or better) or graduate standing. (3 credits)

Quality improvement philosophies; modeling process quality, statistical process control, control charts for variables and attributes, CUSUM and EWMA, short production runs, multivariate quality control, auto correlation, engineering process control, economic design of charts, fill control, pre-control, adaptive schemes, process capability, specifications and tolerances, gage capability studies, acceptance sampling by attributes and variables, and international quality standards.

Advisory prerequisites: Graduate level introductory probability course or permission of instructor. (3 credits)

This course provides a graduate-level introduction to the interdisciplinary field and methods of risk analysis. The course covers the foundations of the field – the meaning of risk and uncertainty; risk perception, communication, and governance; semi-quantitative risk analysis methods; fault trees and event trees; Bayesian belief networks; probability elicitation. It also covers more domain-specific analysis methods from project risk management; terrorism risk analysis, infrastructure risk analysis, and environmental health and safety risk assessment. The focus is on providing a strong foundation for both further study and practice in the field of risk analysis.

Advisory prerequisites: Stats 500 or background in regression. (3 credits) 

Basic design principles, review of analysis of variance, block designs, two-level and three-level factorial and fractional factorial experiments, designs with complex aliasing, data analysis techniques, and case studies, basic response surface methodology, variation reduction, and introductory robust parameter designs.

Advisory prerequisites: IOE master’s student or permission of instructor. (1 credit)

Seminars presented by academic speakers and industry leaders to describe contemporary applications of industrial and operations engineering models and techniques to master’s students in IOE. The focus is on applications but research challenges are addressed as needed. Many speakers also address potential career opportunities for MS students in IOE.