The ORIE program at UT Austin provides a foundation of theory and application, drawing on the expertise of our faculty and researchers. Through coursework and research, students in both the master's and doctoral programs will develop the technical, analytical and managerial skills in modeling, risk-assessment, data analytics, computational methods and many other areas necessary to succeed in a variety of fields.<br>The Operations Research and Industrial Engineering (ORIE) graduate program in the Cockrell School of Engineering at The University of Texas at Austin is a growing, nationally recognized program that fosters a community of collaborative and interdisciplinary faculty and graduate students who focus their research in areas that will make a positive impact on society and its complex systems. ORIE faculty members span disciplines and departments on the UT Austin campus, bringing experience and expertise in areas such as applied mathematics, operations research, decision theory, control and systems engineering, economics, statistics, computer science and finance.<br>At its core, ORIE encompasses a broad range of analytical methodologies and techniques to assess and improve decision-making in a variety of complex systems. The discipline sits at the intersection between humans and the natural and engineered systems they interact with across industries. It's process-based and data-driven, with an eye toward helping real people make better choices.<br>A typical master's program is completed within 18 27 months. Beyond 24 months, your progress will be reviewed to determine ways to ensure your graduation. Finishing the Master's in 18 months is quite doable, with a slight acceleration in the coursework. We have a small percentage of students who elect to finish in 3 semesters (about 18 months) instead of 4.<br>Optimization is broadly focused on the identification of the best solution to complex decision problems that can be represented mathematically. It deals with problems that are complex because there are a large number of alternatives (e.g., scheduling). In some cases, uncertainty is also included and optimization may seek to identify alternatives that perform well across a range of uncertain scenarios.