Methodological research in optimization uses techniques of algebra, geometry, analysis and combinatorics to develop and analyze algorithms for fundamental optimization models having broad applicability. Such models are the means by which we can leverage general-purpose optimization software for applications in all areas. For very large scale applications, specially tailored algorithms are developed.
This area includes:
Integer Optimization: Integer variables are key for modeling logical decisions. Developing algorithms to handle large-scale models with integer variables is an important application-enabling topic. Research in this area makes strong use of geometry, algebra, and combinatorics.
Robust and Stochastic Optimization: Modeling uncertainty in a tractable manner to address applications involving uncertainty by linking data analytics with optimization. Scaling algorithms to handle large instances enables us to make better use of data for decision making. Research in this area makes strong use of geometry, algebra, and analysis.
Combinatorial Optimization and Approximation Algorithms: This area focuses on problems involving combinatorial choices (e.g., network design, facility location, scheduling), with the goal of developing fast and accurate algorithms. Research in this area makes strong use of combinatorics and algebra.
Continuous Optimization: At the heart of many decision problems in engineering and machine learning, and at the core of all kinds of optimization algorithms are continuous optimization problems. Fast, scalable algorithms in this domain have practical ramifications in many contexts.
In partnership with the Detroit Educational Takeover Team, Optimaize Day bridges theoretical understanding of engineering with everyday applications, leaving a lasting impression on the minds of potential future engineers.
Personalized PCA method overcomes the challenges of heterogeneous data analysis
Salar Fattahi discusses his award-winning CAREER proposal which aims to close the current gap between optimization and statistical learning.
Bilevel optimization is used not only in research, but also in business to identify the best solutions for a variety of hierarchical problems. A U-M researcher aims to develop a comprehensive software package that will enable many industries to use this in everyday decision-making.
IOE faculty and students bring in awards from the 2023 Institute for Operations Research and the Management Sciences Annual Meeting.
The Institute for Operations Research and the Management Sciences (INFORMS) annual meeting has officially wrapped up with the University of Michigan Industrial and Operations Engineering (U-M IOE) Department walking away with several awards.
University of Michigan researchers have produced a new prediction model using longitudinal information and deep learning to better predict the return to work time for people with occupational injuries.
University of Michigan Industrial and Operations Engineering (U-M IOE) Assistant Professor, Dr. Salar Fattahi has been awarded $430,556 from the Office of Naval Research (ONR) for scientific research regarding low-rank matrix factorization.
Brian Denton has been appointed the Stephen M. Pollock Collegiate Professor of Industrial and Operations Engineering, named in honor of Stephen M. Pollock, a professor emeritus and former chair of U-M IOE.
U-M IOE PhD student, Kati Moug, has received a 2021 Generation Google Scholarship in recognition of academic performance, leadership, and a commitment to diversity, equity and inclusion.