Maximally Satisfying Lower Quotas in the Hospitals/Residents Problem with Ties

TL;DR

This paper addresses the challenge of satisfying lower quotas in the Hospitals/Residents problem with ties by formulating an optimization approach, analyzing stability variations, and proposing approximation algorithms with proven bounds.

Contribution

It introduces a new optimization framework for maximizing lower quota satisfaction in stable matchings with ties, along with approximation algorithms and complexity results.

Findings

Maximum satisfaction ratios vary across scenarios.

Proposed algorithms outperform naive tie-breaking.

NP-hardness and inapproximability results established.

Abstract

Motivated by the serious problem that hospitals in rural areas suffer from a shortage of residents, we study the Hospitals/Residents model in which hospitals are associated with lower quotas and the objective is to satisfy them as much as possible. When preference lists are strict, the number of residents assigned to each hospital is the same in any stable matching because of the well-known rural hospitals theorem; thus there is no room for algorithmic interventions. However, when ties are introduced to preference lists, this will no longer apply because the number of residents may vary over stable matchings. In this paper, we formulate an optimization problem to find a stable matching with the maximum total satisfaction ratio for lower quotas. We first investigate how the total satisfaction ratio varies over choices of stable matchings in four natural scenarios and provide the exact values of these maximum gaps. Subsequently, we propose a strategy-proof approximation algorithm for our problem; in one scenario it solves the problem optimally, and in the other three scenarios, which are NP-hard, it yields a better approximation factor than that of a naive tie-breaking method. Finally, we show inapproximability results for the above-mentioned three NP-hard scenarios.