September 13, 2018
Data-Driven Integer and Robust Optimization for Scarce Resource Allocation
In the first part of the talk, we present a data-driven optimization approach to estimate wait times for individual patients in the U.S. Kidney Allocation System, based on the very limited system information that they possess in practice. To deal with this information incompleteness, we develop a novel robust optimization analytical framework for wait time estimation in multiclass, multiserver queuing systems. We calibrate our model with highly detailed historical data and illustrate how it can be used to inform medical decision making and improve patient welfare.
In the second part of the talk, we present a data-driven optimization approach for designing fair, efficient, and interpretable policies for prioritizing heterogeneous homeless youth on a waiting list for scarce housing resources. Our framework provides the policy-maker the flexibility to select both their desired structure for the policy and their desired fairness requirements. We evaluate our framework using real-world data from the United States homeless youth housing system. We show that our framework results in policies that are more fair than the current policy in place and than classical interpretable machine learning approaches while achieving a similar (or higher) level of overall efficiency.
The first part of the talk is joint work with Chaitanya Bandi and Nikolaos Trichakis and is forthcoming in Management Science. The second part of the talk is joint work with Mohammad Javad Azizi, Bryan Wilder, Eric Rice, and Milind Tambe and is forthcoming in the Proceedings of the 15th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2018.
Learn more about Prof. Phebe Vayanos.
Location: MB 128