Experimental Statistics
Description: Three credit hours. Three hours of lecture and/or discussion per week. Applications of multiple regression to analysis of variance and experimental designs. Analysis of multiple classifications involving fixed, random, and mixed effects, including crossed and nested variables of classification. Emphasis on computer model applications.
Network Flows and Graphs in Management Science
Description: Three credit hours. Three hours of lecture and discussion per week Principles of network flows and graphs theory and their applications in management science. Classical network flow problem formulations including maximal flow-minimal cut, assignment, transportation and others. Representation of optimization problems as network formulations, and the use of the out of kilter algorithm for their solution. Single versus multi-commodity flow, as well as the relation of graphs and networks to combination problems.
Multiple Regression Analysis
Description: Three credit hours. Three hours of lecture per week. Analysis of unplanned experimental data to develop models for predicting complex systems behavior. Topics include: matrix formulation and properties of least squares estimators in multiple linear regression; analysis of residuals; diagnostics for influential data; strategies for variable selection; diagnostics, effects, and corrective measures for problems with correlated predictor variables; biased regression and other estimation criteria; autocorrelated residuals; simultaneous inference, model validation; use of computer programs to analyze real data and to develop a model.
Human Factors Engineering
Description: Three credit hours. Three hours of lecture and discussion per week. Human factors applications in the design of equipment and work environment. Methods for the analysis of human errors and skills and their utilization in the design of control systems and information displays.
Advanced Production Control
Description: Three credit hours. Three hours of lecture and discussion per week. Advanced topics in forecasting, inventory and applied stochastic processes as they relate to production control systems. Integration of these topics in the production planning process using mathematical optimization techniques and case studies.
Linear and Discrete Optimization
Description: Three credit hours. Three hours of lecture and discussion per week. Basic theory and development of the simplex method for solving linear programming problems with discrete variables. Dual problems and sensitivity analysis. Formulation of problems with discrete variables. Developments of implicit enumeration and related methods for integer problems. Application of linear and discrete optimization methods to problems of industry and government. Use of computer programs.
Systems Simulation
Description: Three credit hours. Three hours of lecture and discussion per week. Principles of feedback dynamics; levels; rates, delays. Simulation languages and their applications in industrial and service systems. Analysis and interpretation of results. Recommendation and justification of proposed alternatives.
Advanced Economics For Engineers
Description: Three credit hours. Three hours of lecture per week. Formulation of economic problems in terms of quantifiable models. Use of deterministic, probabilistic, risk and multiattribute techniques to evaluate design alternatives and to select an acceptable solution.
Introduction to Time Series Analysis
Description: Three credit hours. Three hours of lecture per week. Univariate and bivariate time series in frequency and time domain, use of autocorrelation and spectral analysis for model identification. Uses of model diagnostic and forecasting techniques, dynamic systems modeling and stochasting estimation by means of the Kalman filter.
Material Handling Systems
Description: Three credit hours. Three hours of lecture per week. Fundamentals of material handling systems including types of equipment and their applications, relationship between material handling and design of facilities, computer control, and automation. A project will be required.
Advanced Industrial Experimentation
Description: Three credit hours. Three hours of conference per week. Applications, analogies and differences among confidence intervals, prediction intervals, and tolerance intervals. Fundamental concepts and applications of response surface methodology and evolutionary operations to manufacturing processes. Case study of manufacturing experiments with dichotomous or polytomous response variables. Use of logistic regression for modeling the relationship between a categorical variable and a set of covariates. Effective modeling strategies and the interpretation of results are emphasized. Fundamental concepts in the design and analysis of experiments with mixtures. Statistical techniques and methods for designing, modeling, and analyzing mixture data. Extensive use of software packages for statistical data analysis.
Knowledge Discovery in Engineering Multivariate Data
Description: Three credit hours. Three hours of lecture per week. Development of empirical linear and non-linear model building skills using a variety of tools from multivariate statistics and data mining. Development of skills to identify the model that best represents the natural relationship between a numerical and/or categorical response, and a high-dimensional set of explanatory variables. Special attention is given to data pre-processing, missing value imputation, outlier detection, feature extraction/selection, and model validation. Introduction to unsupervised learning and modeling techniques for multiple response variables.
Mathematical Models in Distribution Logistics
Description: Three credit hours. Three hours of lecture per week. Study on the logistics involved in transporting finished goods from manufacturers to customers. Particular emphasis is given to the design and operation of container terminals, cross-docks, and distribution centers, as well as the management of freight transportation modes. Emphasis will be given on mathematical models for the optimization of distribution systems and their implementation.
Quality Control Systems
Description: Three credit hours. Three hours of lecture per week. Advanced topics in statistical process control. Design of control charts. EWMA charts. The SPRT and its applications in quality engineering: CUSUM and continuous sampling plans. Multivariate control charts. Principles of quality engineering and Taguchi methods. The loss function and its applications to multiresponse experiments.
Special Programs
Description: One to three credit hours. One to three hours of lecture per week. Study of previous work and literature on a selected topic of the industrial engineering field.
Engineering Project
Description: Three to six credit hours. Comprehensive study of a special industrial engineering problem selected so as to integrate the knowledge acquired in the graduate program study. This project fulfills one of the terminal requirements of the Master of Engineering program, and will be governed by the norms established for this purpose.
Thesis
Description: One to six credit hours. Research in the Industrial Engineering field leading to the presentation and approval of a thesis.
Up to three advanced undergraduate (ININ 5XXX) courses can be counted towards a masters degree in Industrial Engineering.
To access the advanced undergraduate course descriptions, click here and scroll down until you find the “Advanced Undergraduate Courses” section (right after ININ 4999).
