Fairness models for multi-agent kidney exchange programmes *

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Klimentova, X (Autor) Outra; Viana, A (Autor) Outra; Joao Pedro Pedroso (Autor) FCUP; Santos, N (Autor) Outra

Revista

Título: Omega

Vol. 102

ISSN: 0305-0483

Editora: Elsevier

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 10 Citações

Outras Informações

ID Authenticus: P-00S-QZX

DOI: 10.1016/j.omega.2020.102333

Abstract (EN): Nowadays there are several countries running independent kidney exchange programmes (KEPs). These programmes allow a patient with kidney failure, having a willing healthy but incompatible donor, to receive a transplant from a similar pair where the donor is compatible with him. Since in general larger patient-donor pools allow for more patients to be matched, this prompts independent programmes (agents) to merge their pools and collaborate in order to increase the overall number of transplants. Such collaboration does however raise a problem: how to assign transplants to agents so that there is a balance between the contribution each agent brings to the merged pool and the benefit it gets from the collaboration. In this paper we propose a new Integer Programming model for multi-agent kidney exchange programmes (mKEPs). It considers the possible existence of multiple optimal solutions in each matching period of a KEP and, in consecutive matching periods, selects the optimal solution among the set of alternative ones in such a way that in the long-term the benefit each agent gets from participating in the mKEP is balanced accordingly to a given criterion. This is done by use of a memory mechanism. Extensive computational tests show the benefit of mKEPs, when compared to independent KEPs, in terms of potential increase in the number of transplants. Furthermore, they show that, under different policies, the number of additional transplants each agent receives can vary significantly. More importantly, results show that the proposed methodology consistently obtains more stable results than methodologies that do not use memory.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14