Below are just a few examples of the types of research projects faculty and graduate students work on in ORIE.

  • Development of a series of algorithms for assigning internal residents to monthly rotations over the year that separates their in-hospital time from the time they spend treating the local underserved population.
  • Development of new, exact algorithms for the mean-standard deviation shortest path problem with independent link costs — ultimately finding the most reliable route between two points for logistics industries such as supply-chain management.
  • Development of tractable non-parametric dynamic programming algorithms that scale to large industrial size problems characterized by a long planning horizon. The emerging data-driven schemes have attractive out-of-sample performance guarantees and have successfully been applied to decision problems in finance, energy systems, engineering and operations management.
  • Formulation of a stochastic optimization model for electric sector capacity planning under climate policy uncertainty. The model identifies hedging strategies featuring investments that perform well over a wide range of policy settings and compares the costs of policy uncertainty with a carbon tax, carbon cap and renewable portfolio standard.
  • Development of algorithms for automated design of electric transmission systems considering various operational constraints such as ability to withstand failure of any single element. This project incorporates renewable variability and other operational constraints into a large optimization formulation.
  • Development of methodologies to better quantify uncertainty in large-scale investment projects. These methodologies were then implemented in tools that decision makers can use on real problems.
  • Integrated modeling of the climate and economic systems to identify the best response to climate change. This work was selected as the best approach to climate change by a panel of four Nobel Laureates in Economics.
  • Creation of statistical models and algorithms to determine the optimal sequence of equipment to use in a semiconductor wafer fabrication process, based on multi-dimensional measurement data. This project uses negative binomial regression models and hurdle count models combined with a weighted ranking algorithm.