Operations Research and Analytics

Operations Research and Analytics Program Area

This program area covers advanced methods for describing, predicting, and optimizing decision-making to improve system performance. Discover how to leverage techniques at the intersection of math, statistics, and computation to build data-driven models fundamental to decision-making in many contexts. Apply mathematical and algorithmic techniques and principles to improve decision-making in a wide range of industries.

Key Topics: Algorithm Design, Computational Modeling, Decision Analysis, Optimization, Queueing Theory, Simulation, Stochastic Systems.

Area Lead: Jon Lee

Foundation Courses

 IOE 510 Linear Programming

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.

 IOE 515 Stochastic Processes

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.

IOE 591 Introduction to Data Analytics

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.

IOE 500 IOE Master’s Seminar

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.

Suggested courses to learn more about the operations research and analytics program area

Optimization

IOE 410 Advanced Optimization and Computational Methods

IOE 511 Continuous Optimization Methods

IOE 512 Dynamic Programming

IOE 614 Integer Programming

IOE 611 Nonlinear Programming

IOE 612 Network Flows

IOE 618 Stochastic Optimization

Stochastic systems

IOE 516 Stochastic Processes II

IOE 545 Stochastic Networks and Operations

IOE 574 Simulation

Data analytics

IOE 465 Design of Experiments

IOE 466 Statistical Quality Control

IOE 473 Advanced Data Analytics

IOE 561 Risk Analysis I

IOE 568 Statistical Learning & Applications in Quality Engineering

IOE 591 Introduction to Data Analytics

IOE 565 Time Series Modeling, Analysis, Forecasting

IOE 691 Bayesian Optimization

Applications-oriented classes

IOE 413 Optimization Modeling in Healthcare

IOE 513 Healthcare Operations Research: Theory and Applications

IOE 517 Game Theory and Operations Applications

IOE 541 Optimization Methods in Supply Chain

IOE 543 Scheduling

IOE552 Financial Engineering I

IOE553 Financial Engineering II