Operations Research and Analytics

Operations Research and Analytics is the application of advanced analytical methods to make better data-driven decisions with less risk. Faculty in this area use mathematical and computer models that incorporate simulation, optimization, probability and statistics to understand complex systems and improve system performance.

The following are examples of initiatives in this area:


Lizhi Wang and his students use optimization techniques to model the national energy, freight, and passenger transportation systems and their interdependencies. Our goal is to balance cost, sustainability, and resiliency of the energy and transportation infrastructures.


Students working with Sigurdur Olafsson invent and analyze new methods for solving complex large-scale optimization problems effectively and efficiently. We apply such methods for finding meaningful patterns in large databases (data mining), such as identifying the most important features for predicting a response. The objective is to find patterns that inform decision making.


Jo Min’s research group utilizes the optimization and equilibrium analyses to obtain managerial insights and economic implications for a closed loop supply chain consisting of a manufacturer, a service provider, and customers. The outcomes of these analyses provide guidelines and recommendations for environmental policy and decision makers regarding re-manufacturability and product life.
A game theoretic model helps Sarah Ryan and her students understand how different decision makers in the fuel and electric energy systems interact. Decisions obtained as outputs of one player’s optimization problem are inputs for the other players’ decision problems. Comparison of equilibria shows how expanding capacity of fuel transportation or electricity transmission affects the total social welfare.