Data-driven Methods to Improve Kidney Allocation and other Resource Allocation Systems

3/9/2017 | 4:15pm | E51-335
Reception to follow.





Nikolaos Trichakis

Assistant Professor

Abstract: In this paper we study systems that allocate different types of scarce resources to heterogeneous allocatees based on predetermined priority rules, e.g., the U.S. deceased-donor kidney allocation system or the public housing program. We tackle the problem of estimating the wait time of an allocatee who possesses incomplete system information with regard, for example, to his relative priority, other allocatees’ preferences, and resource availability. We model such systems as multiclass, multiserver queuing systems that are potentially unstable or in transient regime. We propose a novel robust optimization solution methodology that builds on the assignment problem. For first-come, first-served systems, our approach yields a mixed-integer programming formulation. For the important case where there is a hierarchy in the resource types, we strengthen our formulation through a drastic variable reduction and also propose a highly scalable heuristic, involving only the solution of a convex optimization problem (usually a second-order cone problem). We back the heuristic with a tight approximation guarantee that becomes tighter for larger problem sizes. We illustrate the generalizability of our approach by studying systems that operate under different priority rules, such as class priority. We conduct a wide range of numerical studies, demonstrating that our approach outperforms simulation. We showcase how our methodology can be applied to assist patients in the U.S. deceased donor kidney waitlist. We calibrate our model using historical data to estimate patients’ wait times based on their kidney quality preferences, blood type, location and rank in the waitlist.

Bio: Nikolaos (Nikos) Trichakis is the Zenon Zannetos (1955) Career Development Professor and an Assistant Professor of Operations Management at the MIT Sloan School of Management. His research interests include optimization under uncertainty, data-driven optimization and analytics, with application in healthcare, supply chain management, and finance. Trichakis is also interested in the interplay of fairness and efficiency in resource allocation problems and operations, and the inherent tradeoffs that arise in balancing these objectives. Before his doctoral studies, Trichakis worked in quantitative finance at SunGard APT in London. Prior to joining MIT, Trichakis was on the faculty of Harvard Business School. He received his BS degree from Aristotle University (Greece), and MS degrees from Stanford University and Imperial College (UK), all in electrical and computer engineering. He holds a PhD in operations research from MIT.

Event Time: 

2017 - 16:15