Date Available

3-23-2023

Year of Publication

2023

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Engineering

Department/School/Program

Computer Science

First Advisor

Dr. Judith Goldsmith

Abstract

We present and empirically characterize a general, parallel, heuristic algorithm for computing small ε-Pareto sets. The algorithm can be used as part of a decision support tool for settings in which computing points in objective space is computationally expensive. We use the graph clearing problem, a formalization of indirect organ exchange markets, as a prototypical example setting. We characterize the performance of the algorithm through ε-Pareto set size, ε value provided, and parallel speedup achieved. Our results show that the algorithm's combination of parallel speedup and small ε-Pareto sets is sufficient to be appealing in settings requiring manual review (i.e., those that have a human in the loop) and real-time solutions.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2023.042

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