Year of Publication
Doctor of Philosophy (PhD)
Dr. Judith Goldsmith
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)
Bailey, William, "Small Approximate Pareto Sets with Quality Bounds" (2023). Theses and Dissertations--Computer Science. 127.