Industrial & Operations Engineering

Masters QEAS Option

List of Recommended Courses for IOE MS/MSE Degrees in Quality Engineering and Applied Statistics

Please note that only a subset of the courses listed below will be offered in any given year so that these are advisory only and the courses chosen will depend on availability.

Quality Engineering and Applied Statistics Courses

Courses in this group focus on general methods and techniques for the applied statistics, quality control, reliability analysis and decision making of complex systems

IOE 460. Decision Analysis and Science
This course provides a rigorous introduction to prescriptive decision analysis and more descriptive bounded rationality models. The course starts from the classic axioms of rational choice and covers single-attribute and multi-attribute utility theory, decision trees, the value of information in a decision analytic context, and expert elicitation of both probabilities and utility functions. It then introduces bounded rationality and covers the some of the main classes of bounded rationality and how they can be modeled.

IOE 465. Design and Analysis of Experiments

Prerequisite: IOE 366. (3 credits)

Linear Models, Multi-colinearity and Robust Regression, Comparative Experiments, Randomized Blocks and Latin Squares, Factorial Designs, Confounding, Mixed Level Fractional Factorials, Random and Mixed Models, Nesting and Split Plots, Response Surface Methods, Taguchi Contributions to Experimental Design.

IOE 466 (Mfg 466) (Stat 466). Statistical Quality Control

Prerequisite: IOE 265 (Stat 265) and IOE 366 or Stat 401.
(3 credits)
Quality Improvement Philosophies, Modeling Process Quality, Statistical Process Control, Control Charts for Variables and Attributes, CUSUM and EWMA, Process Capability Analysis, Gage Capability Studies and Variation Decomposition Using Control Chart Methods and ANOVA, Specifications and Tolerances, Six Sigma DMAIC Process and Applications, Acceptance Sampling and other selected advanced topics (Short Production Runs, Multivariate Quality Control, Auto Correlation, Engineering Process Control, Economic Design of Charts and Adaptive Schemes).

IOE 515. Stochastic Processes

Prerequisite: IOE 316. (3 credits)

Introduction to non-measure theoretic stochastic processes. Poisson processes, renewal processes, and discrete time Markov chains. Applications in queuing systems, reliability, and inventory control.

IOE 551. Benchmarking, Productivity Analysis and Performance Measurement
Prerequisite: IOE 510. I (3 credits)
Introduction to quality engineering techniques commonly used for performance measurement, productivity analysis, and identification of best practice. Topics include balanced scorecard, activity-based costing/management, benchmarking, quality function deployment and data envelopment analysis (DEA). Significant focus of the course is on the application of DEA for identification of best practice.

IOE 562 (Stat 535). Reliability

Prerequisite: IOE 316 and IOE 366 or Stat 425 and Stat 426.
(3 credits)
Reliability concepts and methodology for modeling, assessing and improving product reliability: common models for component and system reliability; analysis of field and warranty data; component reliability inference; repairable systems; accelerated stress testing for reliability assessment; reliability improvement through experimental design.

IOE 565 (ME 563) (Mfg 561). Time Series Modeling, Analysis, Forecasting
Prerequisite: IOE 366 or ME 401. I (3 credits)
Time series modeling, analysis, forecasting, and control, identifying parametric time series, autovariance, spectra, Green’s function, trend and seasonality. Examples from manufacturing, quality control, ergonomics, inventory, and management.

IOE 566 (Mfg 569). Advanced Quality Control

Prerequisite: IOE 466. (3 credits)

An applied course on Quality Control including Statistical Process Control Modifications, Linear, Stepwise and Ridge Regression Applications, Multivariate Statistics and Profile Monitoring, Integration of SPC and APC, Sequential Sampling, Sequential Monitoring and Adaptive Schemes, Change Point Detection, Bayesian Optimal Design and Risk Assessment, Causal Modeling and Process Diagnosis, Quality Function Deployment, Tolerancing Systems and Taguchi Methods, Case Studies.


Prerequisites: IOE 265, IOE 366, IOE 466, Stats 500 or equiv

Overview of advanced quality control topics on profile data monitoring and variation diagnosis; Maximum likelihood estimation, Bayes estimation,
mixed model, change point detection methods; Selected supervised, unsupervised, and reinforcement learning methods for data classification, clustering, variation pattern interference and diagnosis; Feature extraction via data transformation (PCA, FFT, wavelets) and variable selection methods, applications with case studies.

IOE 570 (Stat 570). Experimental Design

Prerequisite: Stat 500 or background in regression. Graduate Standing. (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.

IOE 591. Bayesian Data Analysis
This course details the principles of data analysis through a Bayesian lens with modern, computation-based statistical modeling. Bayesian statistics are taught using basic calculus and computer simulation, with some theory.

IOE 591. Risk Analysis
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; extreme value statistics. It also covers more domain-specific analysis methods from human reliability analysis, project risk management; terrorism 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.

Engineering Breadth

Courses in this group include engineering application arenas that illustrate use of the models and analysis methods in Quality Engineering and Applied Statistics.

IOE 441 (Mfg 441). Production and Inventory Control

Prerequisite: IOE 310, IOE 316. (3 credits)

Basic models and techniques for managing inventory systems and for planning production. Topics include deterministic and probabilistic inventory models; production planning and scheduling; and introduction to factory physics.

IOE 447 (Mfg 447). Facility Planning

Prerequisite: IOE 310, IOE 316. (3 credits)

Fundamentals in developing efficient layouts for single-story and multi-story production and service facilities. Manual procedures and microcomputer-based layout algorithms. Algorithms to determine the optimum location of facilities. Special considerations for multi-period, dynamic layout problems.

IOE 449 (Mfg 449). Material Handling Systems

Prerequisite: IOE 310, IOE 316. (2 credits)

Review of material handling equipment used in warehousing and manufacturing. Algorithms to design and analyze discrete parts material storage and flow systems such as Automated Storage/Retrieval Systems, order picking, conveyors, automated guided vehicle systems, and carousels.

IOE 541 (Mfg 541). Inventory Analysis and Control

Prerequisite: IOE 310, IOE 316. (3 credits)

Models and techniques for managing inventory systems and for planning production. Topics include single item and multi-item inventory models, production planning and control, and performance evaluation of manufacturing systems.

IOE 543 (Mfg 543). Scheduling

Prerequisite: IOE 316, IOE 310. (3 credits)

The problem of scheduling several tasks over time, including the topics of measures of performance, single-machine sequencing, flow shop scheduling, the job shop problem, and priority dispatching. Integer programming, dynamic programming, and heuristic approaches to various problems are presented.

IOE 545 (Mfg 545). Queueing Networks

Prerequisite: IOE 515 or EECS 501. (3 credits)

Introduction to queuing networks. Topics include product and non-product form networks, exact results and approximations, queuing networks with blocking, and polling systems. Applications from manufacturing and service industries are given as examples.

IOE 549 (Mfg 549). Plant Flow Systems

Prerequisite: IOE 310, IOE 416. (3 credits)

Analytical models for the design and throughput performance evaluation of material handling systems used in discrete parts flow production facilities. Analysis of design and control issues for manual and automated handling systems including lift trucks, micro-load automatic storage/retrieval systems and automated guided vehicle systems.

IOE 574. Simulation Analysis

Prerequisite: IOE 515. (3 credits)

Underlying probabilistic aspects of simulation experiments, statistical methodology for designing simulation experiments and interpreting output. Random number generators, variate and process generation, output analysis, efficiency improvement techniques, simulation and optimization, how commercial simulation software works. Applications from telecommunications, manufacturing statistical analysis.

IOE 583 (ME 583) (Mfg 583) (EECS 566). Scientific Basis for Reconfigurable Manufacturing
Prerequisite: Graduate Standing or permission of instructor. (3 credits)

Principles of reconfigurable manufacturing systems (RMS). Students will be introduced to fundamental theories applicable to RMS synthesis and analysis. Concepts of customization, integrability, modularity, diagnosability, and convertibility. Reconfiguration design theory, life-cycle economics, open- architecture principles, controller configuration, system reliability, multi-sensor monitoring, and stream of variations. Term projects.

IOE 588 (ME 588) (Mfg 588). Assembly Modeling for Design and Manufacturing
Prerequisites: ME 381 and ME 401 or equivalent. (3 credits)
Assembly on product and process. Assembly representation. Assembly sequence. Datum flow chain. Geometric Dimensioning & Tolerancing. Tolerance analysis. Tolerance synthesis. Robust design. Fixturing. Joint design and joining methods. Stream of variation. Auto body assembly case studies.

Additional Recommended Statistical Courses

Stat500 — Applied Statistics
Stat503 — Applied Multivariate Analysis Stat406 — Introduction to Statistical Computing

Projects, Research, and Seminars

IOE 590. Masters Directed Study, Research, and Special Problems

Prerequisite: graduate standing or permission of instructor. (2-4 credits)