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:
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