Operations Research and Analytics

Advisory prerequisites: Math 217, Math 417, or Math 419. (3 credits)

Formulation of problems from the private and public sectors using the mathematical model of linear programming. Development of the simplex algorithm; duality theory and economic interpretations. Post optimality (sensitivity) analysis application and interpretations. Introduction to transportation and assignment problems; special purpose algorithms and advanced computational techniques. Students have opportunities to formulate and solve models developed from more complex case studies and to use various computer programs.

Advisory prerequisites: IOE 316 or Stats 310. (3 credits)

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

Advisory prerequisites: Math 214 or IOE 366. (3 credits)

This course is an introductory graduate course on data analytics. The course introduces fundamental theories and methods for regression analysis and applications. Topics include multiple regression models, generalized linear models, and nonparametric regression models. Concepts of estimation, inference, diagnostics, transformation, regularization, variable selection, and cross-validation are studied. Students have opportunities to formulate statistical models developed from case studies and to use various computer programs.

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.